diff --git a/.ipynb_checkpoints/index-checkpoint.ipynb b/.ipynb_checkpoints/index-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..15236887cdc8bf5e589a6e1cf7e352d60dfaa4e2
--- /dev/null
+++ b/.ipynb_checkpoints/index-checkpoint.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://em.geosci.xyz\"><img src=\"https://www.gge.eonerc.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaakevlz\" style=\"width: 25%; height: 25%\" align=\"right\"></img></a>\n",
+    "\n",
+    "# Jupyter Notebooks für die Veranstaltungen EdgE 1+2\n",
+    "\n",
+    "Die Jupyter Notebooks sind begleitendes Lehrmaterial für den RWTH Kurse \"Einführung in die Geophysikalische Erkundung 1+2\". Es werden verschiedene in den Vorlesungen unterrichtete Themen behandelt. Sämtliche Notebooks laufen direkt auf dem JupyterHub ohne die Notwendigkeit, Python auf dem eigenen Rechner installiert zu haben (einfach auf die jeweiligen Notebooks links in der Leiste klicken). Alternativ können die Notebooks auch lokal verwendet werden. Allerdings benötigt man hierfür gewisse geophysikalische Programme. Die Installation kann in der Dokumentation von **<a href=\"https://github.com/geoscixyz/geosci-labs \">Geoscilabs</a>**, den Urhebern der Notebooks nachgeschlagen werden.\n",
+    "\n",
+    "Weiterführende Literatur ist unten verlinkt:\n",
+    "- **<a href=\"http://gpg.geosci.xyz\">gpg.geosci.xyz</a>**, allgemein für angewandte Geophysik.\n",
+    "- **<a href=\"http://em.geosci.xyz\">em.geosci.xyz</a>**, speziell für elektromagnetische Themen.  \n",
+    "- **<a href=\"https://wiki.seg.org/wiki/Main_Page\">wiki.seg.org</a>**, das offizielle Wiki der  Society of Exploration Geophysicists (SEG).  \n",
+    "\n",
+    "\n",
+    "\n",
+    "Unten sind die betreffenden Notebooks aufgezählt inklusiver einer kurzen Erläuterung zu deren Inhalten. Die Notebooks können durch direktes anklicken der Links geöffnet werden. Bei weiteren Fragen, wendet euch an den Norbert eures Vertrauens (nklitzsch@eonerc.rwth-aachen.de). \n",
+    "\n",
+    "Viel Spaß mit den Notebooks!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[DCIP](#DC-Resistivity-und-Induced-Polarization) | [EMI](#Electromagnetics) | [GPR](#Ground-Penetrating-Radar-(GPR))  | [Magnetics](#Magnetics) | [Seismic](#Seismic) | [Gravity](#Gravity)** \n",
+    "\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "### DC Resistivity und Induced Polarization\n",
+    "- [DC_SurveyDataInversion.ipynb](./Notebooks/dcip/DC_SurveyDataInversion.ipynb): \n",
+    "\n",
+    "This Notebook introduces the fundamentals of DC resistivity surveys. It is divided into 4 major parts:\n",
+    "\n",
+    "1. Investigation of currents, fields, charges and potentials: All governing physical parameters are investigated in the environment of a cylinder target embedded in a homogeneous halfspace. Here, different subsurface characteristics such as cylinder and electrode geometry or resistivities $\\rho$ of the half space and the cylinder can be varied.\n",
+    "\n",
+    "2. Potential differences and Apparent Resistivities: Using the widgets contained in this notebook you will develop a better understand of what values are actually measured in a DC resistivity survey and how these measurements can be processed, plotted, inverted, and interpreted. The principles of Apparent Resistivity are introduced an further depicted in the widget.\n",
+    "\n",
+    "3. Building Pseudosections: 2D profiles are often plotted as pseudo-sections by extending $45^{\\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\\Delta V_{MN}$, $\\rho_a$, or misfit value at the intersection of these lines. Pseudosections build the foundtion of the following inversion. Pseudo-sections of the apparent resistivity can be generated using dipole-dipole, pole-dipole, or dipole-pole arrays to see how survey geometry can distort the size, shape, and location of conductive bodies in a pseudo-section.  \n",
+    "\n",
+    "4. Parametric Inversion: A pseudosection indicates how the parameter varies with location and depth, but it can only be converted into a 2D model by inversion. Inverting the data to find a model which fits the observed data and is geologically reasonable a standard practice. In this final widget you are able to forward model the apparent resistivity of a cylinder embedded in a two layered earth. \n",
+    "\n",
+    "\n",
+    "- [DC_LayeredEarth.ipynb](./Notebooks/dcip/DC_LayeredEarth.ipynb): \n",
+    "\n",
+    "Using the widgets contained in this notebook we will explore the physical principals governing DC resistivity including the behavior of currents, electric field, electric potentials in a two layer earth. The measured data in a DC experiment are potential differences, we will demonstrate how these provide information about subsurface physical properties. (ALREADY IN FIRST NOTEBOOK)\n",
+    "\n",
+    "- [DC_Cylinder_2D.ipynb](./Notebooks/dcip/DC_Cylinder_2D.ipynb): \n",
+    "\n",
+    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes can be measurable on the surface electrodes. Here, we focus on a cylinder target embedded in a halfspace, and investigate what is happening in the earth when static currents are injected. By investigating changes in currents, electric fields, potential, and charges upon different geometry of cylinder and survey, Tx and Rx location, we understand geometric effects of the target for DCR survey (ALREADY IN FIRST NOTEBOOK).\n",
+    "\n",
+    "- [DC_Layer_Cylinder_2D.ipynb](./Notebooks/dcip/DC_Layer_Cylinder_2D.ipynb): \n",
+    "\n",
+    "In some situations the presence of a near surface layer can have large implications for the detectability of targets beneath the layer. If the near surface layer is very conductive current channelling occrurs and when the layer is very resistive it has a shielding effect. In both cases the near surface layer dramatically reduces the strength of currents beneath the layer and therefore also reduces the strength of charge build up on the surface of the target. This notebook is simillary built up like the previous Notebook except for the addition of a near surface layer.\n",
+    "\n",
+    "- [PhyProp_ColeCole.ipynb](./Notebooks/dcip/PhyProp_ColeCole.ipynb): \n",
+    "\n",
+    "Using a simple Cole-Cole model, we parameterize complex resistivity with four parameters: resistivity at zero frequency ($\\rho_0$), chargeability($\\eta$), time constant ($\\tau$), and frequency dependence ($c$). Based upon those parameters, we understand how resistivity and conductivity changes when medium is chargeable both in frequency domain and time domain.\n",
+    "\n",
+    "### Electromagnetics\n",
+    "\n",
+    "#### Frequency domain (FDEM)\n",
+    "- [EM31.ipynb](./Notebooks/em/FEM/EM_EM31.ipynb): \n",
+    "\n",
+    "In this app, we compute apparent resistivity using the response curves for a two-loop Frequency domain system for a two-layer earth. Below figure shows horizontal coplanar (HCP) configuration. \n",
+    "\n",
+    "- [FDEM EM_Pipeline.ipynb](./Notebooks/em/FEM/EM_Pipeline.ipynb): \n",
+    "\n",
+    "In the following app, we consider a loop-loop system with a pipe taget. Here, we simulate two surveys, one where the boom is oriented East-West (EW) and one where the boom is oriented North-South (NS). \n",
+    "\n",
+    "- [FDEM_Planewave_Wholespace.ipynb](./Notebooks/em/FEM/FDEM_Planewave_Wholespace.ipynb): \n",
+    "\n",
+    "We visualizae downward propagating planewave in the homogeneous earth medium. With the three apps: a) Plane wave app, b) Profile app, and c) Polarization ellipse app, we understand fundamental concepts of planewave propagation. \n",
+    "\n",
+    "#### Time Domain (TDEM)\n",
+    "- [TDEM_Groundedsource.ipynb](./Notebooks/em/TEM/TDEM_Groundedsource.ipynb):\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from a grounded source. Both electric currents and magnetic flux will be visualized to undertand physics of grounded source EM. Both charge buildup (galvanic) and EM induction (inductive) will occur at different times. \n",
+    "\n",
+    "- [TDEM_HorizontalLoop_LayeredEarth.ipynb](./Notebooks/em/TEM/TDEM_HorizontalLoop_LayeredEarth.ipynb):\n",
+    "\n",
+    "Here, we show the transient fields and fluxes that result from placing a vertical magnetic dipole (VMD) source over a layered Earth. The transient response in this case refers to the fields and fluxes that are produced once a long-standing primary magnetic field is removed. There are [two commonly used models](https://em.geosci.xyz/content/maxwell1_fundamentals/dipole_sources_in_homogeneous_media/magnetic_dipole_time/index.html) for describing the VMD source that produces a transient response (both models are used in the Notebook): \n",
+    "\n",
+    "1) as an infinitessimally small bar magnet that experiences a long-standing vertical magnetization which is then instantaneously removed at $t=0$\n",
+    "\n",
+    "2) as an infinitessimally small horizontal loop of wire carrying a constant current which is then instantaneously shut off at $t=0$ (step-off current waveform).\n",
+    "\n",
+    "- [TDEM_InductiveSource.ipynb](./Notebooks/em/TEM/TDEM_InductiveSource.ipynb):\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from inductive sources. Both electric currents and magnetic flux will be visualized to understand physics of inductive source EM. \n",
+    "\n",
+    "\n",
+    "\n",
+    "### Ground Penetrating Radar (GPR)\n",
+    "\n",
+    "- [GPR_Attenuation.ipynb](./Notebooks/gpr/GPR_Attenuation.ipynb):\n",
+    "\n",
+    "This Notebook focuses on the principles of EM wave attenuation. To simplify the GPR problems, we often assume that we do not have conductivity effect. However, in practice, this is not true. For instance, the earth medium can have considerably high conductivity values. In this case, EM wave attenuates as a function of conductivity ($\\sigma$), permittivity ($\\epsilon$), and frequency ($f$). How these factors influence attenuation is investigated in this Notebook.\n",
+    "\n",
+    "- [GPR_Lab6_FitData.ipynb](./Notebooks/gpr/GPR_Lab6_FitData.ipynb):\n",
+    "\n",
+    "This notebook contains two apps:\n",
+    "\n",
+    "+ **Pipe Fitting App**: This app simulates the radargram signature from a cylindrical pipe and lays it over a set of field collected data.\n",
+    "+ **Slab Fitting App**: This app simulates the radargram signature from a rectangular slab and lays it over a set of field collected data.\n",
+    "\n",
+    "By using the models provided (pipe/slab) to fit data signatures within field collected radargram data, we can determine the existence, location and dimensions of pipes and slabs. You may also use this app to learn how radargram signatures from pipes and rectangular slabs change as the parameters provided are altered.\n",
+    "\n",
+    "- [GPR_TBL4_DOI_Resolution.ipynb](./Notebooks/gpr/GPR_TBL4_DOI_Resolution.ipynb):\n",
+    "\n",
+    "This notebook contains two apps:\n",
+    "+ **GPR Zero Offset App**: This app simulates radargram data from two reflectors buried in a homogeneous Earth. The range of parameter values for this app are set such that we may assume we are operating in the wave regime.\n",
+    "+ **Attenuation App**: This app computes the propagation velocity and skin depth for GPR signals as a function of operating frequency.\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Magnetics\n",
+    "\n",
+    "- [Mag_Dipole.ipynb](./Notebooks/mag/Mag_Dipole.ipynb): \n",
+    "\n",
+    "Define a magnetic dipole, the Earth's magneitc field and the observation point to compute 3D plots of field lines and data. This notebook aims to provide the basic principles of magnetic methods in geophysics.\n",
+    "\n",
+    "- [MagneticDipoleApplet.ipynb](./Notebooks/mag/MagneticDipoleApplet.ipynb): \n",
+    "\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a small buried dipolar magnet. In geophysics, this simulates the observed anomaly over a buried susceptible sphere that is magnetized by the Earth's magnetic field.\n",
+    "\n",
+    "- [MagneticPrismApplet.ipynb](./Notebooks/mag/MagneticPrismApplet.ipynb): \n",
+    "\n",
+    "From the Magnetic Dipole applet, we have learned how anomalous magnetic field observed at ground's surface look\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a retangular susceptible prism. \n",
+    "\n",
+    "- [Mag_Induced2D.ipynb](./Notebooks/mag/Mag_Induced2D.ipynb): \n",
+    "\n",
+    "An induced magnetic anomaly can be modelled in this Notebook. The model of the is a rectangular prism. Its geometry and the height of the survey grid above the ground can be adjusted. Moreover, you can change the Earth's field characteristics as well. Based on the prism that you made as well as the defined Earth's magnetic field, the total magnetic field at the receiver locations is computed. In the end, a 2D map and a profile line is produced. \n",
+    "\n",
+    "- [Mag_FitProfile.ipynb](./Notebooks/mag/Mag_FitProfile.ipynb): \n",
+    "\n",
+    "In this Notebook, the fit of one magnetic profile from field observation can be performed.\n",
+    "\n",
+    "### Seismic\n",
+    "- [SeismicApplet.ipynb](./Notebooks/seismic/SeismicApplet.ipynb): \n",
+    "\n",
+    "This Notebooks allows you to model a simple subsurface model to interactively explore seismic raypaths dpending on the applied parameters.\n",
+    "\n",
+    "- [Seis_Refraction.ipynb](./Notebooks/seismic/Seis_Refraction.ipynb): \n",
+    "\n",
+    "A Seismic refraction survey is demonstrated. In this notebook, we will use synthetic seismic data to examine the impact of survey parameters on the expected seismic data.\n",
+    "\n",
+    "- [Seis_Reflection.ipynb](./Notebooks/seismic/Seis_Reflection.ipynb): \n",
+    "\n",
+    "A synthetic reflection seismogram is produced. This Notebook aims to introduce you to the basic principles of reflection seismics. This Notebook also includes vertical resolution and NMO correction widgets. \n",
+    "\n",
+    "- [Seis_NMO.ipynb](./Notebooks/seismic/Seis_NMO.ipynb): \n",
+    "\n",
+    "Consider a reflection event on a CMP gather. The difference between the two-way time at a given offset and the two-way zero-offset time is called normal moveout (NMO). Reflection traveltimes must be corrected for NMO prior to summing the traces in the CMP gather along the offset axis. \n",
+    "We have two CMP gathers generated from different geologic models. One data set is clean and the other is contaminated with noise. In this notebook, we will walk through how to construct a normal incidence seismogram from these data sets. The processing steps include plotting the data, fitting a hyperbola to the reflection event in the data, performin the NMO correction and stacking.\n",
+    "\n",
+    "- [Seis_VerticalResolution.ipynb](./Notebooks/seismic/Seis_VerticalResolution.ipynb): \n",
+    "\n",
+    "When referring to vertical resolution, the question whether two arrivals (one from the top, and one from the bottom of the layer) can be distinguished. In this Notebook, adjust the layer thickness for the middle layer and the frequency of the input pulse to investigate vertical resolution. You can also add noise to the trace. \n",
+    "\n",
+    "- [fourier_transform.ipynb](./Notebooks/seismic/fourier_transform.ipynb): \n",
+    "\n",
+    "In the world of seismology, we use the *Fourier transformation* to transform a signal from the time domain into the frequency domain. That means, we split up the signal and separate the content of each frequency from each other. Doing so, we can analyse our signal according to energy content per frequency. We can extract information on how much amplitude each frequency contributes to the final signal. \n",
+    "\n",
+    "- [2D-LinearInversion-Crosswell-Tomorgraphy.ipynb](./Notebooks/seismic/2D-LinearInversion-Crosswell-Tomorgraphy.ipynb):\n",
+    "\n",
+    "Real world geophysical inverse problems are multidimensional (2D or 3D). This extension of dimension allows us to put more apriori (or geologic) information through the regularization term.  In this notebook, we explore these multidimensional aspects of the linear inversion by using 2D traveltime croswell tomography example. \n",
+    "\n",
+    "\n",
+    "### Gravity\n",
+    "- [gravitySphere.ipynb](./Notebooks/gravity/gravitySphere.ipynb): \n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a sphere buried in the subsurface.\n",
+    "\n",
+    "- [gravityDike.ipynb](./Notebooks/gravity/gravityDike.ipynb): \n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a 2D dipping dike that is infinite along one horizontal direction and buried in the subsurface. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### <center>We love open source!</center>\n",
+    "\n",
+    "<center><a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" width=60 src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a> \n",
+    "\n",
+    "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Dockerfile b/Dockerfile
index c9f8b3aa18a94be92efdd4abf0755670dfb1a2f8..c15a7aff4092c0fb283357f5ef4851662e30803d 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,15 +4,15 @@
 ARG BASE_IMAGE=registry.git-ce.rwth-aachen.de/jupyter/singleuser/python:latest
 FROM ${BASE_IMAGE}
 
-## Install packages via requirements.txt
-#ADD requirements.txt .
-#RUN pip install -r requirements.txt
+# Install packages via requirements.txt
+ADD requirements.txt .
+RUN pip install -r requirements.txt
 
-# .. Or update conda base environment to match specifications in environment.yml
-ADD environment.yml /tmp/environment.yml
+## .. Or update conda base environment to match specifications in environment.yml
+#ADD environment.yml /tmp/environment.yml
 
-# All packages specified in environment.yml are installed in the base environment
-RUN conda env update -f /tmp/environment.yml && \
-    conda clean -a -f -y
-
-ENV JUPYTER_ENABLE_LAB=yes
+## All packages specified in environment.yml are installed in the base environment
+#RUN conda env update -f /tmp/environment.yml && \
+#    conda clean -a -f -y
+#
+#ENV JUPYTER_ENABLE_LAB=yes
diff --git a/dcip/.ipynb_checkpoints/DC_Cylinder_2D-checkpoint.ipynb b/Notebooks/dcip/.ipynb_checkpoints/DC_Cylinder_2D-checkpoint.ipynb
similarity index 97%
rename from dcip/.ipynb_checkpoints/DC_Cylinder_2D-checkpoint.ipynb
rename to Notebooks/dcip/.ipynb_checkpoints/DC_Cylinder_2D-checkpoint.ipynb
index e7761f73e51a01178c042c1655765a02d528dac3..eb453acb57daef4404e9f2862b3f70f39dfdc5ea 100644
--- a/dcip/.ipynb_checkpoints/DC_Cylinder_2D-checkpoint.ipynb
+++ b/Notebooks/dcip/.ipynb_checkpoints/DC_Cylinder_2D-checkpoint.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
     "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
+    "can be measurable on the surface electrodes. \n",
     "Here, we focus on a cylinder target embedded in a halfspace, and investigate what are happening in the earth when static currents are injected. Different from a sphere case, which is a finite target, \"coupling\" among Tx, target (conductor or resistor), and Rx will be significanlty different upon various scenarios. \n",
     "By investigating changes in currents, electric fields, potential, and charges upon different geometry of cylinder and survey, Tx and Rx location, we understand geometric effects of the target for DCR survey. "
    ]
@@ -93,7 +93,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "794335acaae64ac3af46b49c929e5297",
+       "model_id": "b796445f4b82417c827d983f8edf6991",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/dcip/DC_Layer_Cylinder_2D.ipynb b/Notebooks/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2D-checkpoint.ipynb
similarity index 100%
rename from dcip/DC_Layer_Cylinder_2D.ipynb
rename to Notebooks/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2D-checkpoint.ipynb
diff --git a/dcip/.ipynb_checkpoints/DC_LayeredEarth-checkpoint.ipynb b/Notebooks/dcip/.ipynb_checkpoints/DC_LayeredEarth-checkpoint.ipynb
similarity index 90%
rename from dcip/.ipynb_checkpoints/DC_LayeredEarth-checkpoint.ipynb
rename to Notebooks/dcip/.ipynb_checkpoints/DC_LayeredEarth-checkpoint.ipynb
index f4ac24a4f792ddb818f9962f6fcea4a373cd7826..7551b9e739cfbd0bf21136241b77575f65f68ea7 100644
--- a/dcip/.ipynb_checkpoints/DC_LayeredEarth-checkpoint.ipynb
+++ b/Notebooks/dcip/.ipynb_checkpoints/DC_LayeredEarth-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,9 +89,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "504132f7176e42208fea61102f80a1f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(FloatSlider(value=-30.0, continuous_update=False, description='A', max=40.0, min=-40.0, step=1…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "out = DCLayers.plot_layer_potentials_app()\n",
     "display(out)"
diff --git a/dcip/.ipynb_checkpoints/DC_SurveyDataInversion-checkpoint.ipynb b/Notebooks/dcip/.ipynb_checkpoints/DC_SurveyDataInversion-checkpoint.ipynb
similarity index 71%
rename from dcip/.ipynb_checkpoints/DC_SurveyDataInversion-checkpoint.ipynb
rename to Notebooks/dcip/.ipynb_checkpoints/DC_SurveyDataInversion-checkpoint.ipynb
index 80bc2e21edd92e0e9e1ea3d352a12b6affb46c33..503799aafae7f68c938b79941ec21b3213d789c5 100644
--- a/dcip/.ipynb_checkpoints/DC_SurveyDataInversion-checkpoint.ipynb
+++ b/Notebooks/dcip/.ipynb_checkpoints/DC_SurveyDataInversion-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,14 +18,54 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 1. Understanding currents, fields, charges and potentials"
+    "# 1. Understanding currents, fields, charges and potentials\n",
+    " \n",
+    "\n",
+    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
+    "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
+    "can be measurable on the surface electrodes. \n",
+    "Here, we focus on a cylinder target embedded in a halfspace, and investigate what are happening in the earth when static currents are injected. Different from a sphere case, which is a finite target, \"coupling\" among Tx, target (conductor or resistor), and Rx will be significanlty different upon various scenarios. \n",
+    "By investigating changes in currents, electric fields, potential, and charges upon different geometry of cylinder and survey, Tx and Rx location, we understand geometric effects of the target for DCR survey. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cylinder app\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DCR_Setup_Cylinder.png?raw=true\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Question"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Is the potential difference measured by a dipole over a conductive (/resisitive) target higher or lower compared to the half-space reference?\n",
+    "- how do the field lines bend in presence of a conductive (/resistive) target?\n",
+    "- Compared to the positive and negative sources (A and B), how are oriented the positive and negative accumulated charges around a conductive (/resistive) target?\n",
+    "- How would you describe the secondary fields pattern? Does it remind you of the response of an object fundamental to electromagnetics?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cylinder app\n",
+    "\n",
+    "## Parameters:\n",
     "\n",
     " - **survey**: Type of survey\n",
     " - **A**: (+) Current electrode  location\n",
@@ -44,9 +84,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3eb996ad78314314a5284bef96244dbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "app = cylinder_app();\n",
     "display(app)"
@@ -115,9 +170,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "caf54886b2f34b16a3ed4f3513036f64",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(FloatSlider(value=-30.0, continuous_update=False, description='A', max=40.0, min=-40.0, step=1…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "app = plot_layer_potentials_app()\n",
     "display(app)"
@@ -136,17 +208,17 @@
     "The figures shown below show how the points in a pseudo-section are plotted for pole-dipole, dipole-pole, and dipole-dipole arrays. The color coding of the dots match those shown in the widget.\n",
     "<br />\n",
     "<br />\n",
-    "<img style=\"float: center; width: 60%; height: 60%\" src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/dc/PoleDipole.png?raw=true\">\n",
+    "<img style=\"float: center; width: 60%; height: 60%\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/dc/PoleDipole.png?raw=true\">\n",
     "<center>Basic skematic for a uniformly spaced pole-dipole array.\n",
     "<br />\n",
     "<br />\n",
     "<br />\n",
-    "<img style=\"float: center; width: 60%; height: 60%\" src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/dc/DipolePole.png?raw=true\">\n",
+    "<img style=\"float: center; width: 60%; height: 60%\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/dc/DipolePole.png?raw=true\">\n",
     "<center>Basic skematic for a uniformly spaced dipole-pole array. \n",
     "<br />\n",
     "<br />\n",
     "<br />\n",
-    "<img style=\"float: center; width: 60%; height: 60%\" src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/dc/DipoleDipole.png?raw=true\">\n",
+    "<img style=\"float: center; width: 60%; height: 60%\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/dc/DipoleDipole.png?raw=true\">\n",
     "<center>Basic skematic for a uniformly spaced dipole-dipole array.\n",
     "<br />\n"
    ]
@@ -160,9 +232,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b9ee38b80ea473ba246d6af4cdce871",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(IntSlider(value=0, description='i', max=17), Output()), layout=Layout(align_items='stretch', d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "app = MidpointPseudoSectionWidget();\n",
     "display(app)"
@@ -190,11 +277,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd1b6c6a344a4033afb1381c057e0fae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=1000.0, description='$…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "app = DC2DPseudoWidget()\n",
     "display(app)"
@@ -230,9 +332,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07d04e134ab240388a3e69b48300d869",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=500.0, description='$\\\\rho_2…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "app = DC2DfwdWidget()\n",
     "display(app)"
diff --git a/dcip/.ipynb_checkpoints/PhyProp_ColeCole-checkpoint.ipynb b/Notebooks/dcip/.ipynb_checkpoints/PhyProp_ColeCole-checkpoint.ipynb
similarity index 81%
rename from dcip/.ipynb_checkpoints/PhyProp_ColeCole-checkpoint.ipynb
rename to Notebooks/dcip/.ipynb_checkpoints/PhyProp_ColeCole-checkpoint.ipynb
index be91aeee5aafa0aabd50d764f0c1fc71e6f61fff..19959b8aa10c40675f93b8800e274ad75a525a7e 100644
--- a/dcip/.ipynb_checkpoints/PhyProp_ColeCole-checkpoint.ipynb
+++ b/Notebooks/dcip/.ipynb_checkpoints/PhyProp_ColeCole-checkpoint.ipynb
@@ -72,28 +72,34 @@
     "\n",
     "## Parameters\n",
     "\n",
-    "- $\\sigma_1$: Conductivity of the first layer (S/m)\n",
+    "- ($t_1; t_2$): time interval (resistivity widget only)\n",
     "\n",
-    "- $\\sigma_2$: Conductivity of the first layer (S/m)\n",
+    "- ($\\eta$): chargeability\n",
     "\n",
-    "- $f$ (Hz): Frequency (Hz)\n",
+    "- ($c$) : frequency exponent\n",
     "\n",
-    "- Type: \n",
+    "- ($\\tau$): relaxation time\n",
     "\n",
-    "    - Reflection: Transmission power as a function of incident angle    \n",
-    "    - Transmission: Transmission power as a function of incident angle    \n",
-    "    - Angle: relationship between $\\theta_i$ and $\\theta_t$"
+    "- ($\\rho_0$): DC resistivity \n",
+    "\n",
+    "- ($\\rho_{inf}$): high frequency resistivity\n",
+    "\n",
+    "- ($\\sigma_0$): DC conductivity \n",
+    "\n",
+    "- ($\\sigma_{inf}$): high frequency conductivity\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1777bc8b41f245ce996db49b8fe1161c",
+       "model_id": "b336c76cb9fb48c285198c3010d51770",
        "version_major": 2,
        "version_minor": 0
       },
@@ -110,7 +116,7 @@
        "<function geoscilabs.dcip.CondUtils.vizColeCole(sigres='sigma', eta=0.1, tau=0.1, c=0.5, t1=800, t2=1400)>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,14 +128,7 @@
     "         sigres = ToggleButtons(options=['sigma','resis']), \n",
     "         t1=FloatText(value=800), \n",
     "         t2=FloatText(value=1400),          \n",
-    "        )\n",
-    "\n",
-    "# eta  = chargability\n",
-    "# t1,t2 = time interval (resistivity only)\n",
-    "# tau = relaxation time\n",
-    "# c = frequency exponent\n",
-    "# rho/sigma0 = DC resistivity/conductivity\n",
-    "# rho/sigma_inf = high frequency resistivity/conductivity"
+    "        )"
    ]
   },
   {
diff --git a/dcip/DC_Cylinder_2D.ipynb b/Notebooks/dcip/DC_Cylinder_2D.ipynb
similarity index 97%
rename from dcip/DC_Cylinder_2D.ipynb
rename to Notebooks/dcip/DC_Cylinder_2D.ipynb
index 8def5e2632dea423dd2da47c454778cf762c7e69..da6e5707c8449efe230baf040df872a44e3a792b 100644
--- a/dcip/DC_Cylinder_2D.ipynb
+++ b/Notebooks/dcip/DC_Cylinder_2D.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
     "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
+    "can be measurable on the surface electrodes. \n",
     "Here, we focus on a cylinder target embedded in a halfspace, and investigate what are happening in the earth when static currents are injected. Different from a sphere case, which is a finite target, \"coupling\" among Tx, target (conductor or resistor), and Rx will be significanlty different upon various scenarios. \n",
     "By investigating changes in currents, electric fields, potential, and charges upon different geometry of cylinder and survey, Tx and Rx location, we understand geometric effects of the target for DCR survey. "
    ]
@@ -93,7 +93,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b1c5dd4f93345a2bc4a6f25573d8bd8",
+       "model_id": "b4d224f33e4b406584bfc5462482c705",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2D-checkpoint.ipynb b/Notebooks/dcip/DC_Layer_Cylinder_2D.ipynb
similarity index 84%
rename from dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2D-checkpoint.ipynb
rename to Notebooks/dcip/DC_Layer_Cylinder_2D.ipynb
index 68581f24148ccae268683a4317f77b05202ad46a..36d21e5553836cc4f3e8354271a3c0f285dfc011 100644
--- a/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2D-checkpoint.ipynb
+++ b/Notebooks/dcip/DC_Layer_Cylinder_2D.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
    "source": [
     "# Setup\n",
     "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/em/DC_ResLayer_Setup.png?raw=true\">"
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DC_ResLayer_Setup.png?raw=true\">"
    ]
   },
   {
@@ -78,11 +78,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3314470c5604306812ba22448b34148",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "app = ResLayer_app()\n",
     "display(app)"
diff --git a/dcip/DC_LayeredEarth.ipynb b/Notebooks/dcip/DC_LayeredEarth.ipynb
similarity index 98%
rename from dcip/DC_LayeredEarth.ipynb
rename to Notebooks/dcip/DC_LayeredEarth.ipynb
index 97c39f9570d6109df78e92d78b414e30402dbeeb..ab5c772d257be2d6e02749365db126541109a294 100644
--- a/dcip/DC_LayeredEarth.ipynb
+++ b/Notebooks/dcip/DC_LayeredEarth.ipynb
@@ -95,7 +95,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c88c93f061c14bd3bac3e246fab8202e",
+       "model_id": "695517f24db7478cbc0f94f32bc20f2f",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/dcip/DC_SurveyDataInversion.ipynb b/Notebooks/dcip/DC_SurveyDataInversion.ipynb
similarity index 86%
rename from dcip/DC_SurveyDataInversion.ipynb
rename to Notebooks/dcip/DC_SurveyDataInversion.ipynb
index 2cfbd83bd6a76e29ea81ea826aa089a104e0352c..503799aafae7f68c938b79941ec21b3213d789c5 100644
--- a/dcip/DC_SurveyDataInversion.ipynb
+++ b/Notebooks/dcip/DC_SurveyDataInversion.ipynb
@@ -18,14 +18,54 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 1. Understanding currents, fields, charges and potentials"
+    "# 1. Understanding currents, fields, charges and potentials\n",
+    " \n",
+    "\n",
+    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
+    "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
+    "can be measurable on the surface electrodes. \n",
+    "Here, we focus on a cylinder target embedded in a halfspace, and investigate what are happening in the earth when static currents are injected. Different from a sphere case, which is a finite target, \"coupling\" among Tx, target (conductor or resistor), and Rx will be significanlty different upon various scenarios. \n",
+    "By investigating changes in currents, electric fields, potential, and charges upon different geometry of cylinder and survey, Tx and Rx location, we understand geometric effects of the target for DCR survey. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cylinder app\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DCR_Setup_Cylinder.png?raw=true\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Question"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Is the potential difference measured by a dipole over a conductive (/resisitive) target higher or lower compared to the half-space reference?\n",
+    "- how do the field lines bend in presence of a conductive (/resistive) target?\n",
+    "- Compared to the positive and negative sources (A and B), how are oriented the positive and negative accumulated charges around a conductive (/resistive) target?\n",
+    "- How would you describe the secondary fields pattern? Does it remind you of the response of an object fundamental to electromagnetics?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cylinder app\n",
+    "\n",
+    "## Parameters:\n",
     "\n",
     " - **survey**: Type of survey\n",
     " - **A**: (+) Current electrode  location\n",
@@ -50,7 +90,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f21de52afbd4823b82c63cf55166c3c",
+       "model_id": "3eb996ad78314314a5284bef96244dbd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -130,13 +170,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "707adbc6365644488ddbfc2d638a3b2b",
+       "model_id": "caf54886b2f34b16a3ed4f3513036f64",
        "version_major": 2,
        "version_minor": 0
       },
@@ -196,7 +238,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a6a81ca95fbb4e31ac89f6f5be4cfc3f",
+       "model_id": "4b9ee38b80ea473ba246d6af4cdce871",
        "version_major": 2,
        "version_minor": 0
       },
@@ -243,7 +285,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b066ab561417408cad84d48e338bf329",
+       "model_id": "dd1b6c6a344a4033afb1381c057e0fae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -296,7 +338,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21dd914ef7e94b0cab60b1a007f162ea",
+       "model_id": "07d04e134ab240388a3e69b48300d869",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/dcip/PhyProp_ColeCole.ipynb b/Notebooks/dcip/PhyProp_ColeCole.ipynb
similarity index 81%
rename from dcip/PhyProp_ColeCole.ipynb
rename to Notebooks/dcip/PhyProp_ColeCole.ipynb
index be91aeee5aafa0aabd50d764f0c1fc71e6f61fff..19959b8aa10c40675f93b8800e274ad75a525a7e 100644
--- a/dcip/PhyProp_ColeCole.ipynb
+++ b/Notebooks/dcip/PhyProp_ColeCole.ipynb
@@ -72,28 +72,34 @@
     "\n",
     "## Parameters\n",
     "\n",
-    "- $\\sigma_1$: Conductivity of the first layer (S/m)\n",
+    "- ($t_1; t_2$): time interval (resistivity widget only)\n",
     "\n",
-    "- $\\sigma_2$: Conductivity of the first layer (S/m)\n",
+    "- ($\\eta$): chargeability\n",
     "\n",
-    "- $f$ (Hz): Frequency (Hz)\n",
+    "- ($c$) : frequency exponent\n",
     "\n",
-    "- Type: \n",
+    "- ($\\tau$): relaxation time\n",
     "\n",
-    "    - Reflection: Transmission power as a function of incident angle    \n",
-    "    - Transmission: Transmission power as a function of incident angle    \n",
-    "    - Angle: relationship between $\\theta_i$ and $\\theta_t$"
+    "- ($\\rho_0$): DC resistivity \n",
+    "\n",
+    "- ($\\rho_{inf}$): high frequency resistivity\n",
+    "\n",
+    "- ($\\sigma_0$): DC conductivity \n",
+    "\n",
+    "- ($\\sigma_{inf}$): high frequency conductivity\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1777bc8b41f245ce996db49b8fe1161c",
+       "model_id": "b336c76cb9fb48c285198c3010d51770",
        "version_major": 2,
        "version_minor": 0
       },
@@ -110,7 +116,7 @@
        "<function geoscilabs.dcip.CondUtils.vizColeCole(sigres='sigma', eta=0.1, tau=0.1, c=0.5, t1=800, t2=1400)>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,14 +128,7 @@
     "         sigres = ToggleButtons(options=['sigma','resis']), \n",
     "         t1=FloatText(value=800), \n",
     "         t2=FloatText(value=1400),          \n",
-    "        )\n",
-    "\n",
-    "# eta  = chargability\n",
-    "# t1,t2 = time interval (resistivity only)\n",
-    "# tau = relaxation time\n",
-    "# c = frequency exponent\n",
-    "# rho/sigma0 = DC resistivity/conductivity\n",
-    "# rho/sigma_inf = high frequency resistivity/conductivity"
+    "        )"
    ]
   },
   {
diff --git a/Notebooks/em/FEM/.ipynb_checkpoints/EM_EM31-checkpoint.ipynb b/Notebooks/em/FEM/.ipynb_checkpoints/EM_EM31-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c2d1244267a58adae5f6b6d7e34efe2548f8fdbb
--- /dev/null
+++ b/Notebooks/em/FEM/.ipynb_checkpoints/EM_EM31-checkpoint.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.em.ResponseFct import interactive_responseFct\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computing Apparent Resistivity\n",
+    "\n",
+    "In this app, we compute apparent resistivity using the response curves for a two-loop Frequency domain system for a two-layer earth. Below figure shows horizontal coplanar (HCP) configuration. \n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/ResponseFct/ResponseFct.png?raw=true\"> </img>\n",
+    "\n",
+    "Assuming the coil spacing $s \\ll \\delta$, where $\\delta$ is the skin depth, the apparent conductivity is given by\n",
+    "\n",
+    "$$\n",
+    "\\sigma_a = \\int_0^\\infty \\phi(z) \\sigma(z) dz\n",
+    "$$\n",
+    "\n",
+    "Where \n",
+    " - $\\sigma_a$ is the apparent conductivity\n",
+    " - $\\phi$ is the response function\n",
+    " - $\\sigma$ is the conductivity structure\n",
+    "\n",
+    "Note that in the following plots, the y-axis is a normalized depth: $z/s$ where $s$ is the source-receiver separation.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two different configurations of source-receiver configurations are considered:\n",
+    "\n",
+    "- HCP: Horizontal coplanar system. The associated dipoles are perpendicular to the plane of the loops and are therefore in the vertical direction. The response function associated with this is .\n",
+    "\n",
+    "- VCP: Vertical coplanar system. The associated dipoles are perpendicular to the plane of the loops and are therefore in the horizontal direction. The response function associated with this is .\n",
+    "\n",
+    "For more, see the <a href=\"http://gpg.geosci.xyz/en/latest/content/electromagnetics/dual_loop_systems.html\">GPG section on dual loop systems</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameters:\n",
+    "\n",
+    "- h$_{boom}$: height of the source-receiver boom from the surface [m]\n",
+    "\n",
+    "- h$_{1}$: thickness of the first layer [m]\n",
+    "\n",
+    "- $\\sigma_{1}$: conductivity of the first layer [S/m]\n",
+    "\n",
+    "- $\\sigma_{2}$: conductivity of the second layer [S/m]\n",
+    "\n",
+    "- configuration: configuration of the source-receiver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e8a0992d60047b691fd233e41a9f8ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='$h_{boom}$', max=2.0), Floa…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app = interactive_responseFct()\n",
+    "display(app)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/FEM/.ipynb_checkpoints/EM_Pipeline-checkpoint.ipynb b/Notebooks/em/FEM/.ipynb_checkpoints/EM_Pipeline-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6ac19ffde41c4d264d15c32e40064a15a8ac5a69
--- /dev/null
+++ b/Notebooks/em/FEM/.ipynb_checkpoints/EM_Pipeline-checkpoint.ipynb
@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Expo site characterization using EM-31\n",
+    "\n",
+    "\n",
+    "## Import Necessary Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.em.FDEMpipe import interact_femPipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pipe Widget\n",
+    "\n",
+    "In the following app, we consider a loop-loop system with a pipe taget. Here, we simulate two surveys, one where the boom is oriented East-West (EW) and one where the boom is oriented North-South (NS). \n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/FEMpipe/model.png?raw=true\" style=\"width: 40%; height: 40%\"> </img>\n",
+    "\n",
+    "The variables are:\n",
+    "\n",
+    "- alpha: \n",
+    "$$\\alpha = \\frac{\\omega L}{R} = \\frac{2\\pi f L}{R}$$\n",
+    "- pipedepth: Depth of the pipe center\n",
+    "\n",
+    "We plot the percentage of Hp/Hs ratio in the Widget. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2696f379819743caba556444fac4839a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='alpha', max=5.0, min=0.1), …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pipe = interact_femPipe()\n",
+    "pipe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/FEM/.ipynb_checkpoints/FDEM_Planewave_Wholespace-checkpoint.ipynb b/Notebooks/em/FEM/.ipynb_checkpoints/FDEM_Planewave_Wholespace-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ba9d571bf4c99e9dd061437dc8c7b246f190b66f
--- /dev/null
+++ b/Notebooks/em/FEM/.ipynb_checkpoints/FDEM_Planewave_Wholespace-checkpoint.ipynb
@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "c59bfd9b-4293-433e-83db-820c33f4c378"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from IPython.display import display\n",
+    "from geoscilabs.em.PlanewaveWidgetFD import PlanewaveWidget, PolarEllipse, InteractivePlaneProfile\n",
+    "from geoscilabs.em.DipoleWidgetFD import InteractiveDipoleProfile\n",
+    "from geoscilabs.em.VolumeWidgetPlane import InteractivePlanes, plotObj3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 4,
+        "hidden": true,
+        "row": 6,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "95f1e819-0749-42ff-ad94-6d428298a5a7"
+    }
+   },
+   "source": [
+    "# Planewave propagation in a Whole-space (frequency-domain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": true,
+        "row": 6,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "3bd63ed4-b758-48e5-a662-b68e4b2ce034"
+    }
+   },
+   "source": [
+    "# Purpose\n",
+    "\n",
+    "We visualizae downward propagating planewave in the homogeneous earth medium. With the three apps: a) Plane wave app, b) Profile app, and c) Polarization ellipse app, we understand fundamental concepts of planewave propagation. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Set up\n",
+    "\n",
+    "Planewave EM equation can be written as \n",
+    "\n",
+    "$$\\frac{\\partial^2 \\mathbf{E}}{\\partial z^2} + k^2 \\mathbf{E} = 0,$$\n",
+    "\n",
+    "For homogeneous earth, solution can be simply derived:\n",
+    "\n",
+    "\n",
+    "$$\\mathbf{E} = \\mathbf{E}_0 e^{ikz}$$\n",
+    "\n",
+    "$$\\mathbf{H} = - i \\omega \\mu \\nabla \\times (\\mathbf{E}_0 e^{ikz}).$$\n",
+    "\n",
+    "where complex wavenumber $k$ is \n",
+    "\n",
+    "$$ k = \\sqrt{\\mu \\epsilon \\omega^2 - i \\mu \\sigma \\omega}.$$\n",
+    "\n",
+    "In time domain, the wave travelling in the negative z-direction has the form:\n",
+    "\n",
+    "$$ \\mathbf{e} = \\mathbf{e}_0^- e^{i(k z + \\omega t)}.$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 21,
+        "hidden": false,
+        "row": 0,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "baf63d98-9356-4c2d-81d7-8f8cd1a6da2d"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plotObj3D()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 14,
+        "hidden": true,
+        "row": 14,
+        "width": null
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "4cca45b8-6d74-43f2-b3aa-2f1f3ece54f3"
+    }
+   },
+   "source": [
+    "# Planewave app\n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- Field: Type of EM fields (\"Ex\": electric field, \"Hy\": magnetic field)\n",
+    "- AmpDir: Type of the vectoral EM fields \n",
+    "\n",
+    "    None: $F_x$ or $F_y$ or $F_z$\n",
+    "    \n",
+    "    Amp: $\\mathbf{F} \\cdot \\mathbf{F}^* = |\\mathbf{F}|^2$\n",
+    "    \n",
+    "    Dir: Real part of a vectoral EM fields, $\\Re[\\mathbf{F}]$\n",
+    "    \n",
+    "- ComplexNumber: Type of complex data (\"Re\", \"Im\", \"Amp\", \"Phase\")    \n",
+    "- Frequency: Transmitting frequency (Hz)\n",
+    "- Sigma: Conductivity of homogeneous earth (S/m)\n",
+    "- Scale: Choose \"log\" or \"linear\" scale \n",
+    "- Time: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 21,
+        "hidden": false,
+        "row": 0,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "d4efa881-fa5c-4ecc-87b0-47f53e865a5f"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b371dec79c8744d4a4db2b068455ad9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='Field', options=('Ex', 'Hy'), value='Ex'), ToggleButtons(desc…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dwidget = PlanewaveWidget()\n",
+    "Q = dwidget.InteractivePlaneWave()\n",
+    "display(Q)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": true,
+        "row": 13,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Profile app\n",
+    "\n",
+    "We visualize EM fields at vertical profile (marked as red dots in the above app). \n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- **Field**: Ex, Hy, and Impedance \n",
+    "- ** $\\sigma$ **: Conductivity (S/m)\n",
+    "- **Scale**: Log10 or Linear scale\n",
+    "- **Fixed**: Fix the scale or not\n",
+    "- **$f$**: Frequency\n",
+    "- **$t$**: Time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 17,
+        "hidden": false,
+        "row": 21,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "125cc6340f1b40cd8e322f62ba9fb736",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='Field', options=('Ex', 'Hy', 'Impedance', 'rhophi'), value='E…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(InteractivePlaneProfile())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 4,
+        "hidden": true,
+        "row": 14,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Polarization Ellipse app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 13,
+        "hidden": false,
+        "row": 38,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "348ec372c8e241e191ff47a74d372a29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='itime', max=999, step=10), Output()), _dom_classes=('wid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Polarwidget = PolarEllipse(); \n",
+    "Polarwidget.Interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
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+    }
+   }
+  },
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+}
diff --git a/Notebooks/em/FEM/EM_EM31.ipynb b/Notebooks/em/FEM/EM_EM31.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1df3f2a7ec94b4ce1fdb7eef4e5de00c0f4cc1c0
--- /dev/null
+++ b/Notebooks/em/FEM/EM_EM31.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.em.ResponseFct import interactive_responseFct\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computing Apparent Resistivity\n",
+    "\n",
+    "In this app, we compute apparent resistivity using the response curves for a two-loop Frequency domain system for a two-layer earth. Below figure shows horizontal coplanar (HCP) configuration. \n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/ResponseFct/ResponseFct.png?raw=true\"> </img>\n",
+    "\n",
+    "Assuming the coil spacing $s \\ll \\delta$, where $\\delta$ is the skin depth, the apparent conductivity is given by\n",
+    "\n",
+    "$$\n",
+    "\\sigma_a = \\int_0^\\infty \\phi(z) \\sigma(z) dz\n",
+    "$$\n",
+    "\n",
+    "Where \n",
+    " - $\\sigma_a$ is the apparent conductivity\n",
+    " - $\\phi$ is the response function\n",
+    " - $\\sigma$ is the conductivity structure\n",
+    "\n",
+    "Note that in the following plots, the y-axis is a normalized depth: $z/s$ where $s$ is the source-receiver separation.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Two different configurations of source-receiver configurations are considered:\n",
+    "\n",
+    "- HCP: Horizontal coplanar system. The associated dipoles are perpendicular to the plane of the loops and are therefore in the vertical direction. The response function associated with this is .\n",
+    "\n",
+    "- VCP: Vertical coplanar system. The associated dipoles are perpendicular to the plane of the loops and are therefore in the horizontal direction. The response function associated with this is .\n",
+    "\n",
+    "For more, see the <a href=\"http://gpg.geosci.xyz/en/latest/content/electromagnetics/dual_loop_systems.html\">GPG section on dual loop systems</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameters:\n",
+    "\n",
+    "- h$_{boom}$: height of the source-receiver boom from the surface [m]\n",
+    "\n",
+    "- h$_{1}$: thickness of the first layer [m]\n",
+    "\n",
+    "- $\\sigma_{1}$: conductivity of the first layer [S/m]\n",
+    "\n",
+    "- $\\sigma_{2}$: conductivity of the second layer [S/m]\n",
+    "\n",
+    "- configuration: configuration of the source-receiver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a6039d69c87430ca8655a992074c9e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='$h_{boom}$', max=2.0), Floa…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app = interactive_responseFct()\n",
+    "display(app)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
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+   "language": "python",
+   "name": "python3"
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/FEM/EM_Pipeline.ipynb b/Notebooks/em/FEM/EM_Pipeline.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6ac19ffde41c4d264d15c32e40064a15a8ac5a69
--- /dev/null
+++ b/Notebooks/em/FEM/EM_Pipeline.ipynb
@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Expo site characterization using EM-31\n",
+    "\n",
+    "\n",
+    "## Import Necessary Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.em.FDEMpipe import interact_femPipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pipe Widget\n",
+    "\n",
+    "In the following app, we consider a loop-loop system with a pipe taget. Here, we simulate two surveys, one where the boom is oriented East-West (EW) and one where the boom is oriented North-South (NS). \n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/FEMpipe/model.png?raw=true\" style=\"width: 40%; height: 40%\"> </img>\n",
+    "\n",
+    "The variables are:\n",
+    "\n",
+    "- alpha: \n",
+    "$$\\alpha = \\frac{\\omega L}{R} = \\frac{2\\pi f L}{R}$$\n",
+    "- pipedepth: Depth of the pipe center\n",
+    "\n",
+    "We plot the percentage of Hp/Hs ratio in the Widget. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2696f379819743caba556444fac4839a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='alpha', max=5.0, min=0.1), …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pipe = interact_femPipe()\n",
+    "pipe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/FEM/FDEM_Planewave_Wholespace.ipynb b/Notebooks/em/FEM/FDEM_Planewave_Wholespace.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ba9d571bf4c99e9dd061437dc8c7b246f190b66f
--- /dev/null
+++ b/Notebooks/em/FEM/FDEM_Planewave_Wholespace.ipynb
@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "hidden": true
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "c59bfd9b-4293-433e-83db-820c33f4c378"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from IPython.display import display\n",
+    "from geoscilabs.em.PlanewaveWidgetFD import PlanewaveWidget, PolarEllipse, InteractivePlaneProfile\n",
+    "from geoscilabs.em.DipoleWidgetFD import InteractiveDipoleProfile\n",
+    "from geoscilabs.em.VolumeWidgetPlane import InteractivePlanes, plotObj3D"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 4,
+        "hidden": true,
+        "row": 6,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "95f1e819-0749-42ff-ad94-6d428298a5a7"
+    }
+   },
+   "source": [
+    "# Planewave propagation in a Whole-space (frequency-domain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": true,
+        "row": 6,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "3bd63ed4-b758-48e5-a662-b68e4b2ce034"
+    }
+   },
+   "source": [
+    "# Purpose\n",
+    "\n",
+    "We visualizae downward propagating planewave in the homogeneous earth medium. With the three apps: a) Plane wave app, b) Profile app, and c) Polarization ellipse app, we understand fundamental concepts of planewave propagation. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Set up\n",
+    "\n",
+    "Planewave EM equation can be written as \n",
+    "\n",
+    "$$\\frac{\\partial^2 \\mathbf{E}}{\\partial z^2} + k^2 \\mathbf{E} = 0,$$\n",
+    "\n",
+    "For homogeneous earth, solution can be simply derived:\n",
+    "\n",
+    "\n",
+    "$$\\mathbf{E} = \\mathbf{E}_0 e^{ikz}$$\n",
+    "\n",
+    "$$\\mathbf{H} = - i \\omega \\mu \\nabla \\times (\\mathbf{E}_0 e^{ikz}).$$\n",
+    "\n",
+    "where complex wavenumber $k$ is \n",
+    "\n",
+    "$$ k = \\sqrt{\\mu \\epsilon \\omega^2 - i \\mu \\sigma \\omega}.$$\n",
+    "\n",
+    "In time domain, the wave travelling in the negative z-direction has the form:\n",
+    "\n",
+    "$$ \\mathbf{e} = \\mathbf{e}_0^- e^{i(k z + \\omega t)}.$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 21,
+        "hidden": false,
+        "row": 0,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "baf63d98-9356-4c2d-81d7-8f8cd1a6da2d"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plotObj3D()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 14,
+        "hidden": true,
+        "row": 14,
+        "width": null
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "4cca45b8-6d74-43f2-b3aa-2f1f3ece54f3"
+    }
+   },
+   "source": [
+    "# Planewave app\n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- Field: Type of EM fields (\"Ex\": electric field, \"Hy\": magnetic field)\n",
+    "- AmpDir: Type of the vectoral EM fields \n",
+    "\n",
+    "    None: $F_x$ or $F_y$ or $F_z$\n",
+    "    \n",
+    "    Amp: $\\mathbf{F} \\cdot \\mathbf{F}^* = |\\mathbf{F}|^2$\n",
+    "    \n",
+    "    Dir: Real part of a vectoral EM fields, $\\Re[\\mathbf{F}]$\n",
+    "    \n",
+    "- ComplexNumber: Type of complex data (\"Re\", \"Im\", \"Amp\", \"Phase\")    \n",
+    "- Frequency: Transmitting frequency (Hz)\n",
+    "- Sigma: Conductivity of homogeneous earth (S/m)\n",
+    "- Scale: Choose \"log\" or \"linear\" scale \n",
+    "- Time: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 21,
+        "hidden": false,
+        "row": 0,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "nbpresent": {
+     "id": "d4efa881-fa5c-4ecc-87b0-47f53e865a5f"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b371dec79c8744d4a4db2b068455ad9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='Field', options=('Ex', 'Hy'), value='Ex'), ToggleButtons(desc…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dwidget = PlanewaveWidget()\n",
+    "Q = dwidget.InteractivePlaneWave()\n",
+    "display(Q)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 4,
+        "height": 4,
+        "hidden": true,
+        "row": 13,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Profile app\n",
+    "\n",
+    "We visualize EM fields at vertical profile (marked as red dots in the above app). \n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- **Field**: Ex, Hy, and Impedance \n",
+    "- ** $\\sigma$ **: Conductivity (S/m)\n",
+    "- **Scale**: Log10 or Linear scale\n",
+    "- **Fixed**: Fix the scale or not\n",
+    "- **$f$**: Frequency\n",
+    "- **$t$**: Time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 17,
+        "hidden": false,
+        "row": 21,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "125cc6340f1b40cd8e322f62ba9fb736",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='Field', options=('Ex', 'Hy', 'Impedance', 'rhophi'), value='E…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(InteractivePlaneProfile())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 8,
+        "height": 4,
+        "hidden": true,
+        "row": 14,
+        "width": 4
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "# Polarization Ellipse app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 13,
+        "hidden": false,
+        "row": 38,
+        "width": 8
+       },
+       "report_default": {}
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "348ec372c8e241e191ff47a74d372a29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='itime', max=999, step=10), Output()), _dom_classes=('wid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Polarwidget = PolarEllipse(); \n",
+    "Polarwidget.Interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
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+}
diff --git a/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_Groundedsource-checkpoint.ipynb b/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_Groundedsource-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a5d9c2505582dd3533564a781a93d7358bf2c6b7
--- /dev/null
+++ b/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_Groundedsource-checkpoint.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, interactive, FloatSlider, IntSlider, ToggleButtons\n",
+    "from geoscilabs.em.TDEMGroundedSource import choose_model, load_or_run_results, PlotTDEM\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring fields from a grounded source"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from a grounded source. Both electric currents and magnetic flux will be visualized to undertand physics of grounded source EM. Both charge buildup (galvanic) and EM induction (inductive) will occur at different times. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load simulation results (or run)\n",
+    "\n",
+    "Three models are considered here. \n",
+    "\n",
+    "- Halfspace (0.01 S/m)\n",
+    "- Conductive block in halfspace (1 S/m)\n",
+    "- Resitive block in halfspace (10$^{-4}$ S/m)\n",
+    "\n",
+    "Using below widget, you can choose a model that you want to explore. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4af9cb7d009c48b6917d7b456df63b4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='model', options=('halfspace', 'conductor', 'resistor'), value…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q = interact(choose_model, \n",
+    "         model=ToggleButtons(\n",
+    "             options=[\"halfspace\", \"conductor\", \"resistor\"], value=\"halfspace\"\n",
+    "         )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then here we are going to load results. If you want to rerun, you can set `re_run` as `True`. \n",
+    "With that option, you can change conductivity value of the block and halfspace you can alter values for `sigma_halfspace` and `sigma_block`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file already exists, new file is called C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_groundedsource.tar\n",
+      "Downloading https://storage.googleapis.com/simpeg/em_examples/tdem_groundedsource/tdem_groundedsource.tar\n",
+      "   saved to: C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_groundedsource.tar\n",
+      "Download completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rcParams['font.size']=16\n",
+    "options = load_or_run_results(\n",
+    "    re_run=False, #better just run the results, high computational effort\n",
+    "    fname=choose_model(Q.widget.kwargs['model']),\n",
+    "    sigma_block=0.01,\n",
+    "    sigma_halfspace=0.01\n",
+    ")\n",
+    "tdem = PlotTDEM(**options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c886e50d880145879205836ffc5b59cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=30.0, description='elev', max=180.0, min=-180.0, step=10.0), FloatSlid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.show_3d_survey_geometry, \n",
+    "    elev=FloatSlider(min=-180, max=180, step=10, value=30),\n",
+    "    azim=FloatSlider(min=-180, max=180, step=10, value=-45),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cc44e813f5d4028bf01e919a754462f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, continuous_update=False, description='itime', max=50, min=15), Toggl…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_input_currents, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, continuous_update=False),\n",
+    "    scale=ToggleButtons(\n",
+    "        options=[\"linear\", \"log\"], value=\"linear\"\n",
+    "    ),\n",
+    "    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b816cb64aaa04a369f1d1b9578352fd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, continuous_update=False, description='itime', max=50, min=15), Outpu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_electric_currents, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, continuous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69e4ce557d7746ca9d63f1472609890a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, continuous_update=False, description='itime', max=50, min=15), Outpu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_magnetic_flux, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, continuous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_HorizontalLoop_LayeredEarth-checkpoint.ipynb b/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_HorizontalLoop_LayeredEarth-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a384dc079061f5dcca6db38eea721942cd047235
--- /dev/null
+++ b/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_HorizontalLoop_LayeredEarth-checkpoint.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Horizontal Current Loop over a Layered Earth (time domain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "from IPython.display import display\n",
+    "from geoscilabs.em.TDEMHorizontalLoopCylWidget import TDEMHorizontalLoopCylWidget\n",
+    "APP = TDEMHorizontalLoopCylWidget()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import rcParams\n",
+    "rcParams['font.size'] = 16"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "Here, we show the transient fields and fluxes that result from placing a vertical magnetic dipole (VMD) source over a layered Earth. The transient response in this case refers to the fields and fluxes that are produced once a long-standing primary magnetic field is removed.\n",
+    "\n",
+    "There are [two commonly used models](https://em.geosci.xyz/content/maxwell1_fundamentals/dipole_sources_in_homogeneous_media/magnetic_dipole_time/index.html) for describing the VMD source that produces a transient response: 1) as an infinitessimally small bar magnet that experiences a long-standing vertical magnetization which is then instantaneously removed at $t=0$, and 2) as an infinitessimally small horizontal loop of wire carrying a constant current which is then instantaneously shut off at $t=0$ (step-off current waveform).\n",
+    "\n",
+    "True dipole sources do not exist in nature however they can be approximated in practice. For geophysical applications, we use small current loops to approximate transient VMD sources. These EM sources may be placed on the Earth's surface (ground-based surveys) or flown through the air (airborne surveys). According to the Biot-Savart law, a primary magnetic field is produced whenever there is current in the loop. When the current is shut-off, the sudden change in magnetic flux induces anomalous currents in the Earth which propagate and diffuse over time. The distribution and propagation of the induced currents depends on the subsurface conductivity distribution and how much time has passed since the current in the VMD source was shut off. The induced currents ultimately produce secondary magnetic fields which can be measured by a receiver.\n",
+    "\n",
+    "In this app, we explore the following:\n",
+    "\n",
+    "- How do the fields and currents produced by the transient VMD source change over time?\n",
+    "- For a layered Earth, how does changing layer thickness and conductivity impact the fields and currents produced by the transient VMD source?\n",
+    "- How do the secondary fields measured above the Earth's surface change over time?\n",
+    "- For a layered Earth, how does changing layer thickness and conductivity impact secondary fields measured above the Earth's surface?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "The geological scenario being modeled is shown in the figure below. Here, we assume the Earth is comprised of 3 layers. Each layer can have a different electrical conductivity ($\\sigma$). However, a constant magnetic susceptibility ($\\chi$) is used for all layers; where $\\mu_0$ is the magnetic permeability of free space and $\\mu = \\mu_0 (1 +\\chi)$. The thicknesses of the top two layers are given by $h_1$ and $h_2$, respectively.\n",
+    "\n",
+    "In this case, a transient VMD source (*Tx*) is used to excite the Earth, and the Earth's TEM response (secondary magnetic field) is measured by a receiver (*Rx*). In practice, the transmitter and receiver may be placed near the Earth's surface or in the air. The receiver may also measure secondary fields at a variety of times after the source is shut off.\n",
+    "\n",
+    "To understand the fields and currents resulting from a transient VMD source we have two apps:\n",
+    "\n",
+    "- **Fields app:** Models the fields and currents everywhere at a particular time after shutoff\n",
+    "- **Data app:** Models the secondary magnetic field observed by the receiver as a function of off-time\n",
+    "\n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/LayeredEarthTEM.png?raw=true\"></img>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise\n",
+    "\n",
+    "**Follow the exercise in a linear fashion. Some questions may use parameters set in a previous question.**\n",
+    "\n",
+    "- **Q1:** Set $\\sigma_1$, $\\sigma_2$ and $\\sigma_3$ to arbitrary conductivity values. Based on the geometry of the problem, which components (x, y, z) of each field (E, B, dBdt or J) are zero? Run the *Fields app* and set *AmpDir = None*. Next, try different combinations of *Field* and *Comp*. Does the simulation match what you initially thought?\n",
+    "\n",
+    "\n",
+    "- **Q2:** Re-run the *Fields app* to set parameters back to default. What happens to the *Ey* and *Jy* as you increase *time index* starting at 1? How does the diffusion and propagation of the EM signal change if the conductivity of all the layers is increased to 1 S/m?\n",
+    "\n",
+    "\n",
+    "- **Q3:** Re-run the *Fields app* to set parameters back to default. Set $\\sigma_1 = 0.01$ S/m, $\\sigma_2 = 1$ S/m and $\\sigma_3 = 0.01$ S/m. Now increase *time index* starting at 1. Is the signal able to effectively penetrate the conductive layer? Why/why not? What if the layer was resistive (i.e. $\\sigma_2 = 0.0001$ S/m) instead?\n",
+    "\n",
+    "\n",
+    "- **Q4:** Repeat Q3 but examine the current density. Where is the highest concentration of current density at late time channels? Does this support your answer to Q3?\n",
+    "\n",
+    "\n",
+    "- **Q5:** Re-run the *Fields app* to set parameters back to default. Set *Field = B*, *AmpDir = Direction*. What happens to the magnetic flux density as the *time index* is increased starting at 1? At (x,z)=(0,0), what is the vector direction of the magnetic flux density? Repeat Q5 for dBdt.\n",
+    "\n",
+    "\n",
+    "- **Q6:** Re-run the *Fields app* to set parameters back to default. Set $\\sigma_1 = 0.01$ S/m, $\\sigma_2 = 1$ S/m and $\\sigma_3 = 0.01$ S/m. Examine how B and dBdt are impacted by the conductive layer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fields app\n",
+    "\n",
+    "We use this app to simulate the fields and currents everywhere due to a transient VMD source. The fields and induced currents depend on time and the subsurface conductivity distribution. You will use the app to change various parameters in the model and see how the fields and currents change.\n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- **Update:** If *True* is selected, the simulation is re-run as parameters are being changed. If *False* is selected, the simulation will not be re-fun as parameters are being changed.\n",
+    "- **Field:** Type of EM fields (\"E\": electric field, \"B\": total magnetic flux density, \"dBdt\": time-derivative of the magnetic flux density, \"J\": current density and \"Model\": conductivity model)\n",
+    "- **AmpDir:** If *None* is selected, then the *x*, *y* or *z* component chosen on the next line is plotted. If *Direction* is chosen, a vector plot is plotted (only possible for B and dB/dt)\n",
+    "- **Comp.:** If *None* is selected on the previous line, the user chooses whether the *x*, *y* or *z* component is plotted.\n",
+    "- Time index: The time channel at which fields are being plotted\n",
+    "- $\\boldsymbol{\\sigma_0}$: Conductivity of 0th layer in S/m\n",
+    "- $\\boldsymbol{\\sigma_1}$: Conductivity of 1st layer in S/m\n",
+    "- $\\boldsymbol{\\sigma_2}$: Conductivity of 2nd layer in S/m\n",
+    "- $\\boldsymbol{\\sigma_3}$: Conductivity of 3rd layer in S/m\n",
+    "- $\\boldsymbol{\\chi}$: Susceptibility of 1-3 layers in SI\n",
+    "- $\\boldsymbol{h_1}$: Thickness of the first layer in metres\n",
+    "- $\\boldsymbol{h_2}$: Thickness of the second layer in metres\n",
+    "- **Scale:** Plot data values on *log-scale* or *linear-scale*\n",
+    "- $\\boldsymbol{\\Delta x}$ (m): Horizontal separation distance between the transmitter and receiver\n",
+    "- $\\boldsymbol{\\Delta z}$ (m): Height of the transmitter and receiver above the Earth's surface\n",
+    "- **Time index:** Time index for the set of frequencies models by this app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "694d57600323447c85d64a311340c867",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(Checkbox(value=True, description='Update'), ToggleButtons(description='Field', options=('E', '…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q1 = APP.InteractivePlane_Layer()\n",
+    "display(Q1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data app\n",
+    "\n",
+    "Using this app, we show how the fields observed at the receiver location depend on the parameters set in the previous app. *Note that if you want to see changes in the data due to changes in the model, you MUST* re-run the previous app. \n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- **Field:** Type of EM fields (\"E\": electric field, \"B\": magnetic flux density, \"dBdt\": time-derivative of the magnetic flux density)\n",
+    "- **Comp.:** Direction of EM field at Rx locations        \n",
+    "- **Scale:** Scale of y-axis values (\"log\" or \"linear\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a90c48a35cc7486fae01f58bc1a82333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(ToggleButtons(description='Field', index=1, options=('E', 'B', 'dBdt'), value='B'), ToggleButt…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q2 = APP.InteractiveData_Layer()\n",
+    "display(Q2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Explore\n",
+    "\n",
+    "EM fields will be depenent upon a number of parameters, using a simple half-space model ($\\sigma_1=\\sigma_2=\\sigma_3$) explore how EM fields and data changes upon below four parameters. \n",
+    "\n",
+    "- E1: Effects of frequency?\n",
+    "\n",
+    "\n",
+    "- E2: Effects of Tx height?\n",
+    "\n",
+    "\n",
+    "- E3: Effects of Conductivity?\n",
+    "\n",
+    "\n",
+    "- E4: Effects of Susceptibility?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "c0dd4dbce2ff4d0cacf7363e6fdfed13": {
+     "views": [
+      {
+       "cell_index": 6
+      }
+     ]
+    },
+    "d6ee822b25404d33979ba6ec5f19963c": {
+     "views": [
+      {
+       "cell_index": 8
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_InductiveSource-checkpoint.ipynb b/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_InductiveSource-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bfd056888e048f99c9a541574a16b6dd4aacda82
--- /dev/null
+++ b/Notebooks/em/TEM/.ipynb_checkpoints/TDEM_InductiveSource-checkpoint.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\tobia\\anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning: pylab import has clobbered these variables: ['interactive']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ipywidgets import interact, interactive, FloatSlider, IntSlider, ToggleButtons\n",
+    "from geoscilabs.em.TDEMInductiveSource import choose_source, load_or_run_results, PlotTDEM\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring fields from inductive sources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from inductive sources. Both electric currents and magnetic flux will be visualized to understand physics of inductive source EM. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load simulation results (or run)\n",
+    "\n",
+    "Two inductive sources are considered:\n",
+    "\n",
+    "- Vertical magnetic dipole(VMD)\n",
+    "- Horizontal magnetic dipole(HMD)\n",
+    "\n",
+    "Using below widget, you can choose a model that you want to explore. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8014266cd4349248b0c4d4a83e26516",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='src_type', options=('VMD', 'HMD'), value='VMD'), Output()), _…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q = interact(choose_source, \n",
+    "         src_type=ToggleButtons(\n",
+    "             options=[\"VMD\", \"HMD\"], value=\"VMD\"\n",
+    "         )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then here we are going to load results. If you want to rerun, you can set `re_run` as `True`. \n",
+    "With that option, you can change conductivity value of the block and halfspace you can alter a value for `sigma_halfspace`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file already exists, new file is called C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_inductivesource.tar\n",
+      "Downloading https://storage.googleapis.com/simpeg/em_examples/tdem_inductivesource/tdem_inductivesource.tar\n",
+      "   saved to: C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_inductivesource.tar\n",
+      "Download completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rcParams['font.size']=16\n",
+    "options = load_or_run_results(\n",
+    "    re_run=False, #better just run the results, high computational effort\n",
+    "    fname=choose_source(Q.widget.kwargs['src_type']),\n",
+    "    src_type=Q.widget.kwargs['src_type'],\n",
+    "    sigma_halfspace=0.01\n",
+    ")\n",
+    "tdem = PlotTDEM(**options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfbb28c42ea44bbdbab975d4ed41ed41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=30.0, description='elev', max=180.0, min=-180.0, step=10.0), FloatSlid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.show_3d_survey_geometry, \n",
+    "    elev=FloatSlider(min=-180, max=180, step=10, value=30),\n",
+    "    azim=FloatSlider(min=-180, max=180, step=10, value=-45),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff6dca3d9ba046a9a022d828daba8081",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, description='itime', max=50, min=15), ToggleButtons(description='sca…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_input_currents, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, contiusous_update=False),\n",
+    "    scale=ToggleButtons(\n",
+    "        options=[\"linear\", \"log\"], value=\"linear\"\n",
+    "    ),\n",
+    "    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ef2cf16465f4b0694cea8a877904118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, description='itime', max=50, min=10, step=2), Output()), _dom_classe…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_electric_currents, \n",
+    "    itime=IntSlider(min=10, max=50, step=2, value=10, contiusous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e45f7f54007748e99845855aff5a0a78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, description='itime', max=50, min=10, step=2), Output()), _dom_classe…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_magnetic_flux, \n",
+    "    itime=IntSlider(min=10, max=50, step=2, value=10, contiusous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/TEM/TDEM_Groundedsource.ipynb b/Notebooks/em/TEM/TDEM_Groundedsource.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a5d9c2505582dd3533564a781a93d7358bf2c6b7
--- /dev/null
+++ b/Notebooks/em/TEM/TDEM_Groundedsource.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, interactive, FloatSlider, IntSlider, ToggleButtons\n",
+    "from geoscilabs.em.TDEMGroundedSource import choose_model, load_or_run_results, PlotTDEM\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring fields from a grounded source"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from a grounded source. Both electric currents and magnetic flux will be visualized to undertand physics of grounded source EM. Both charge buildup (galvanic) and EM induction (inductive) will occur at different times. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load simulation results (or run)\n",
+    "\n",
+    "Three models are considered here. \n",
+    "\n",
+    "- Halfspace (0.01 S/m)\n",
+    "- Conductive block in halfspace (1 S/m)\n",
+    "- Resitive block in halfspace (10$^{-4}$ S/m)\n",
+    "\n",
+    "Using below widget, you can choose a model that you want to explore. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4af9cb7d009c48b6917d7b456df63b4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='model', options=('halfspace', 'conductor', 'resistor'), value…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q = interact(choose_model, \n",
+    "         model=ToggleButtons(\n",
+    "             options=[\"halfspace\", \"conductor\", \"resistor\"], value=\"halfspace\"\n",
+    "         )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then here we are going to load results. If you want to rerun, you can set `re_run` as `True`. \n",
+    "With that option, you can change conductivity value of the block and halfspace you can alter values for `sigma_halfspace` and `sigma_block`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file already exists, new file is called C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_groundedsource.tar\n",
+      "Downloading https://storage.googleapis.com/simpeg/em_examples/tdem_groundedsource/tdem_groundedsource.tar\n",
+      "   saved to: C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_groundedsource.tar\n",
+      "Download completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rcParams['font.size']=16\n",
+    "options = load_or_run_results(\n",
+    "    re_run=False, #better just run the results, high computational effort\n",
+    "    fname=choose_model(Q.widget.kwargs['model']),\n",
+    "    sigma_block=0.01,\n",
+    "    sigma_halfspace=0.01\n",
+    ")\n",
+    "tdem = PlotTDEM(**options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c886e50d880145879205836ffc5b59cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=30.0, description='elev', max=180.0, min=-180.0, step=10.0), FloatSlid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.show_3d_survey_geometry, \n",
+    "    elev=FloatSlider(min=-180, max=180, step=10, value=30),\n",
+    "    azim=FloatSlider(min=-180, max=180, step=10, value=-45),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cc44e813f5d4028bf01e919a754462f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, continuous_update=False, description='itime', max=50, min=15), Toggl…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_input_currents, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, continuous_update=False),\n",
+    "    scale=ToggleButtons(\n",
+    "        options=[\"linear\", \"log\"], value=\"linear\"\n",
+    "    ),\n",
+    "    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b816cb64aaa04a369f1d1b9578352fd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, continuous_update=False, description='itime', max=50, min=15), Outpu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_electric_currents, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, continuous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69e4ce557d7746ca9d63f1472609890a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, continuous_update=False, description='itime', max=50, min=15), Outpu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_magnetic_flux, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, continuous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/TEM/TDEM_HorizontalLoop_LayeredEarth.ipynb b/Notebooks/em/TEM/TDEM_HorizontalLoop_LayeredEarth.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2ff5a53f0b9a302176436c8d710a3805ee1a170e
--- /dev/null
+++ b/Notebooks/em/TEM/TDEM_HorizontalLoop_LayeredEarth.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Horizontal Current Loop over a Layered Earth (time domain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "from IPython.display import display\n",
+    "from geoscilabs.em.TDEMHorizontalLoopCylWidget import TDEMHorizontalLoopCylWidget\n",
+    "APP = TDEMHorizontalLoopCylWidget()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import rcParams\n",
+    "rcParams['font.size'] = 16"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "Here, we show the transient fields and fluxes that result from placing a vertical magnetic dipole (VMD) source over a layered Earth. The transient response in this case refers to the fields and fluxes that are produced once a long-standing primary magnetic field is removed.\n",
+    "\n",
+    "There are [two commonly used models](https://em.geosci.xyz/content/maxwell1_fundamentals/dipole_sources_in_homogeneous_media/magnetic_dipole_time/index.html) for describing the VMD source that produces a transient response: 1) as an infinitessimally small bar magnet that experiences a long-standing vertical magnetization which is then instantaneously removed at $t=0$, and 2) as an infinitessimally small horizontal loop of wire carrying a constant current which is then instantaneously shut off at $t=0$ (step-off current waveform).\n",
+    "\n",
+    "True dipole sources do not exist in nature however they can be approximated in practice. For geophysical applications, we use small current loops to approximate transient VMD sources. These EM sources may be placed on the Earth's surface (ground-based surveys) or flown through the air (airborne surveys). According to the Biot-Savart law, a primary magnetic field is produced whenever there is current in the loop. When the current is shut-off, the sudden change in magnetic flux induces anomalous currents in the Earth which propagate and diffuse over time. The distribution and propagation of the induced currents depends on the subsurface conductivity distribution and how much time has passed since the current in the VMD source was shut off. The induced currents ultimately produce secondary magnetic fields which can be measured by a receiver.\n",
+    "\n",
+    "In this app, we explore the following:\n",
+    "\n",
+    "- How do the fields and currents produced by the transient VMD source change over time?\n",
+    "- For a layered Earth, how does changing layer thickness and conductivity impact the fields and currents produced by the transient VMD source?\n",
+    "- How do the secondary fields measured above the Earth's surface change over time?\n",
+    "- For a layered Earth, how does changing layer thickness and conductivity impact secondary fields measured above the Earth's surface?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "\n",
+    "The geological scenario being modeled is shown in the figure below. Here, we assume the Earth is comprised of 3 layers. Each layer can have a different electrical conductivity ($\\sigma$). However, a constant magnetic susceptibility ($\\chi$) is used for all layers; where $\\mu_0$ is the magnetic permeability of free space and $\\mu = \\mu_0 (1 +\\chi)$. The thicknesses of the top two layers are given by $h_1$ and $h_2$, respectively.\n",
+    "\n",
+    "In this case, a transient VMD source (*Tx*) is used to excite the Earth, and the Earth's TEM response (secondary magnetic field) is measured by a receiver (*Rx*). In practice, the transmitter and receiver may be placed near the Earth's surface or in the air. The receiver may also measure secondary fields at a variety of times after the source is shut off.\n",
+    "\n",
+    "To understand the fields and currents resulting from a transient VMD source we have two apps:\n",
+    "\n",
+    "- **Fields app:** Models the fields and currents everywhere at a particular time after shutoff\n",
+    "- **Data app:** Models the secondary magnetic field observed by the receiver as a function of off-time\n",
+    "\n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/LayeredEarthTEM.png?raw=true\"></img>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise\n",
+    "\n",
+    "**Follow the exercise in a linear fashion. Some questions may use parameters set in a previous question.**\n",
+    "\n",
+    "- **Q1:** Set $\\sigma_1$, $\\sigma_2$ and $\\sigma_3$ to arbitrary conductivity values. Based on the geometry of the problem, which components (x, y, z) of each field (E, B, dBdt or J) are zero? Run the *Fields app* and set *AmpDir = None*. Next, try different combinations of *Field* and *Comp*. Does the simulation match what you initially thought?\n",
+    "\n",
+    "\n",
+    "- **Q2:** Re-run the *Fields app* to set parameters back to default. What happens to the *Ey* and *Jy* as you increase *time index* starting at 1? How does the diffusion and propagation of the EM signal change if the conductivity of all the layers is increased to 1 S/m?\n",
+    "\n",
+    "\n",
+    "- **Q3:** Re-run the *Fields app* to set parameters back to default. Set $\\sigma_1 = 0.01$ S/m, $\\sigma_2 = 1$ S/m and $\\sigma_3 = 0.01$ S/m. Now increase *time index* starting at 1. Is the signal able to effectively penetrate the conductive layer? Why/why not? What if the layer was resistive (i.e. $\\sigma_2 = 0.0001$ S/m) instead?\n",
+    "\n",
+    "\n",
+    "- **Q4:** Repeat Q3 but examine the current density. Where is the highest concentration of current density at late time channels? Does this support your answer to Q3?\n",
+    "\n",
+    "\n",
+    "- **Q5:** Re-run the *Fields app* to set parameters back to default. Set *Field = B*, *AmpDir = Direction*. What happens to the magnetic flux density as the *time index* is increased starting at 1? At (x,z)=(0,0), what is the vector direction of the magnetic flux density? Repeat Q5 for dBdt.\n",
+    "\n",
+    "\n",
+    "- **Q6:** Re-run the *Fields app* to set parameters back to default. Set $\\sigma_1 = 0.01$ S/m, $\\sigma_2 = 1$ S/m and $\\sigma_3 = 0.01$ S/m. Examine how B and dBdt are impacted by the conductive layer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fields app\n",
+    "\n",
+    "We use this app to simulate the fields and currents everywhere due to a transient VMD source. The fields and induced currents depend on time and the subsurface conductivity distribution. You will use the app to change various parameters in the model and see how the fields and currents change.\n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- **Update:** If *True* is selected, the simulation is re-run as parameters are being changed. If *False* is selected, the simulation will not be re-fun as parameters are being changed.\n",
+    "- **Field:** Type of EM fields (\"E\": electric field, \"B\": total magnetic flux density, \"dBdt\": time-derivative of the magnetic flux density, \"J\": current density and \"Model\": conductivity model)\n",
+    "- **AmpDir:** If *None* is selected, then the *x*, *y* or *z* component chosen on the next line is plotted. If *Direction* is chosen, a vector plot is plotted (only possible for B and dB/dt)\n",
+    "- **Comp.:** If *None* is selected on the previous line, the user chooses whether the *x*, *y* or *z* component is plotted.\n",
+    "- Time index: The time channel at which fields are being plotted\n",
+    "- $\\boldsymbol{\\sigma_0}$: Conductivity of 0th layer in S/m\n",
+    "- $\\boldsymbol{\\sigma_1}$: Conductivity of 1st layer in S/m\n",
+    "- $\\boldsymbol{\\sigma_2}$: Conductivity of 2nd layer in S/m\n",
+    "- $\\boldsymbol{\\sigma_3}$: Conductivity of 3rd layer in S/m\n",
+    "- $\\boldsymbol{\\chi}$: Susceptibility of 1-3 layers in SI\n",
+    "- $\\boldsymbol{h_1}$: Thickness of the first layer in metres\n",
+    "- $\\boldsymbol{h_2}$: Thickness of the second layer in metres\n",
+    "- **Scale:** Plot data values on *log-scale* or *linear-scale*\n",
+    "- $\\boldsymbol{\\Delta x}$ (m): Horizontal separation distance between the transmitter and receiver\n",
+    "- $\\boldsymbol{\\Delta z}$ (m): Height of the transmitter and receiver above the Earth's surface\n",
+    "- **Time index:** Time index for the set of frequencies models by this app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6efb6028efd64a11b5cececc2579c9e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(Checkbox(value=True, description='Update'), ToggleButtons(description='Field', options=('E', '…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q1 = APP.InteractivePlane_Layer()\n",
+    "display(Q1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data app\n",
+    "\n",
+    "Using this app, we show how the fields observed at the receiver location depend on the parameters set in the previous app. *Note that if you want to see changes in the data due to changes in the model, you MUST* re-run the previous app. \n",
+    "\n",
+    "## Parameters:\n",
+    "\n",
+    "- **Field:** Type of EM fields (\"E\": electric field, \"B\": magnetic flux density, \"dBdt\": time-derivative of the magnetic flux density)\n",
+    "- **Comp.:** Direction of EM field at Rx locations        \n",
+    "- **Scale:** Scale of y-axis values (\"log\" or \"linear\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d35d874e87f486d81659bcf2bed2ee7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "MyApp(children=(ToggleButtons(description='Field', index=1, options=('E', 'B', 'dBdt'), value='B'), ToggleButt…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q2 = APP.InteractiveData_Layer()\n",
+    "display(Q2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Explore\n",
+    "\n",
+    "EM fields will be depenent upon a number of parameters, using a simple half-space model ($\\sigma_1=\\sigma_2=\\sigma_3$) explore how EM fields and data changes upon below four parameters. \n",
+    "\n",
+    "- E1: Effects of frequency?\n",
+    "\n",
+    "\n",
+    "- E2: Effects of Tx height?\n",
+    "\n",
+    "\n",
+    "- E3: Effects of Conductivity?\n",
+    "\n",
+    "\n",
+    "- E4: Effects of Susceptibility?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "c0dd4dbce2ff4d0cacf7363e6fdfed13": {
+     "views": [
+      {
+       "cell_index": 6
+      }
+     ]
+    },
+    "d6ee822b25404d33979ba6ec5f19963c": {
+     "views": [
+      {
+       "cell_index": 8
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/TEM/TDEM_InductiveSource.ipynb b/Notebooks/em/TEM/TDEM_InductiveSource.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bfd056888e048f99c9a541574a16b6dd4aacda82
--- /dev/null
+++ b/Notebooks/em/TEM/TDEM_InductiveSource.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\tobia\\anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning: pylab import has clobbered these variables: ['interactive']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ipywidgets import interact, interactive, FloatSlider, IntSlider, ToggleButtons\n",
+    "from geoscilabs.em.TDEMInductiveSource import choose_source, load_or_run_results, PlotTDEM\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring fields from inductive sources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from inductive sources. Both electric currents and magnetic flux will be visualized to understand physics of inductive source EM. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load simulation results (or run)\n",
+    "\n",
+    "Two inductive sources are considered:\n",
+    "\n",
+    "- Vertical magnetic dipole(VMD)\n",
+    "- Horizontal magnetic dipole(HMD)\n",
+    "\n",
+    "Using below widget, you can choose a model that you want to explore. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8014266cd4349248b0c4d4a83e26516",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButtons(description='src_type', options=('VMD', 'HMD'), value='VMD'), Output()), _…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q = interact(choose_source, \n",
+    "         src_type=ToggleButtons(\n",
+    "             options=[\"VMD\", \"HMD\"], value=\"VMD\"\n",
+    "         )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then here we are going to load results. If you want to rerun, you can set `re_run` as `True`. \n",
+    "With that option, you can change conductivity value of the block and halfspace you can alter a value for `sigma_halfspace`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file already exists, new file is called C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_inductivesource.tar\n",
+      "Downloading https://storage.googleapis.com/simpeg/em_examples/tdem_inductivesource/tdem_inductivesource.tar\n",
+      "   saved to: C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\em\\TEM\\tdem_inductivesource.tar\n",
+      "Download completed!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rcParams['font.size']=16\n",
+    "options = load_or_run_results(\n",
+    "    re_run=False, #better just run the results, high computational effort\n",
+    "    fname=choose_source(Q.widget.kwargs['src_type']),\n",
+    "    src_type=Q.widget.kwargs['src_type'],\n",
+    "    sigma_halfspace=0.01\n",
+    ")\n",
+    "tdem = PlotTDEM(**options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfbb28c42ea44bbdbab975d4ed41ed41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=30.0, description='elev', max=180.0, min=-180.0, step=10.0), FloatSlid…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.show_3d_survey_geometry, \n",
+    "    elev=FloatSlider(min=-180, max=180, step=10, value=30),\n",
+    "    azim=FloatSlider(min=-180, max=180, step=10, value=-45),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff6dca3d9ba046a9a022d828daba8081",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=15, description='itime', max=50, min=15), ToggleButtons(description='sca…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_input_currents, \n",
+    "    itime=IntSlider(min=15, max=50, step=1, value=15, contiusous_update=False),\n",
+    "    scale=ToggleButtons(\n",
+    "        options=[\"linear\", \"log\"], value=\"linear\"\n",
+    "    ),\n",
+    "    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ef2cf16465f4b0694cea8a877904118",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, description='itime', max=50, min=10, step=2), Output()), _dom_classe…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_electric_currents, \n",
+    "    itime=IntSlider(min=10, max=50, step=2, value=10, contiusous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e45f7f54007748e99845855aff5a0a78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, description='itime', max=50, min=10, step=2), Output()), _dom_classe…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(\n",
+    "    tdem.plot_magnetic_flux, \n",
+    "    itime=IntSlider(min=10, max=50, step=2, value=10, contiusous_update=False)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/em/TEM/tdem_groundedsource.tar b/Notebooks/em/TEM/tdem_groundedsource.tar
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diff --git a/gpr/.ipynb_checkpoints/GPR_Attenuation-checkpoint.ipynb b/Notebooks/gpr/.ipynb_checkpoints/GPR_Attenuation-checkpoint.ipynb
similarity index 100%
rename from gpr/.ipynb_checkpoints/GPR_Attenuation-checkpoint.ipynb
rename to Notebooks/gpr/.ipynb_checkpoints/GPR_Attenuation-checkpoint.ipynb
diff --git a/gpr/.ipynb_checkpoints/GPR_Lab6_FitData-checkpoint.ipynb b/Notebooks/gpr/.ipynb_checkpoints/GPR_Lab6_FitData-checkpoint.ipynb
similarity index 88%
rename from gpr/.ipynb_checkpoints/GPR_Lab6_FitData-checkpoint.ipynb
rename to Notebooks/gpr/.ipynb_checkpoints/GPR_Lab6_FitData-checkpoint.ipynb
index ad8d903034ff53c29f448d60676c7097d6c503a0..eb235d5d53136f58e72e0c03fe827ffc713720e2 100644
--- a/gpr/.ipynb_checkpoints/GPR_Lab6_FitData-checkpoint.ipynb
+++ b/Notebooks/gpr/.ipynb_checkpoints/GPR_Lab6_FitData-checkpoint.ipynb
@@ -34,7 +34,10 @@
    "source": [
     "%matplotlib inline\n",
     "from geoscilabs.gpr.GPRlab1 import downloadRadargramImage, PipeWidget, WallWidget\n",
-    "from SimPEG.utils import download"
+    "from SimPEG.utils import download\n",
+    "\n",
+    "import os\n",
+    "from PIL import Image"
    ]
   },
   {
@@ -57,13 +60,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30003d3acf5343ffa9ab616b53ae5ea7",
+       "model_id": "5114d488ae354db19842dc1c412e0567",
        "version_major": 2,
        "version_minor": 0
       },
@@ -80,14 +83,18 @@
        "<function geoscilabs.gpr.GPRlab1.PipeWidgetFcn(epsr, h, xc, r, imgcmp)>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
-    "radargramImage = downloadRadargramImage(URL)\n",
+    "#URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
+    "URL = os.getcwd()+ \"/ubc_GPRdata.png\"\n",
+    "\n",
+    "#radargramImage = downloadRadargramImage(URL)\n",
+    "radargramImage = Image.open(URL)\n",
+    "\n",
     "PipeWidget(radargramImage)"
    ]
   },
@@ -111,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -119,7 +126,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7cf4887036f047e4a2a4e8c958734e19",
+       "model_id": "0e40457b4ed34dc58b654d19c9725c60",
        "version_major": 2,
        "version_minor": 0
       },
@@ -136,14 +143,18 @@
        "<function geoscilabs.gpr.GPRlab1.WallWidgetFcn(epsr, h, x1, x2, imgcmp)>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
-    "radargramImage = downloadRadargramImage(URL)\n",
+    "#URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
+    "#radargramImage = downloadRadargramImage(URL)\n",
+    "\n",
+    "URL = os.getcwd()+ \"/ubc_GPRdata.png\"\n",
+    "radargramImage = Image.open(URL)\n",
+    "\n",
     "WallWidget(radargramImage)"
    ]
   },
diff --git a/gpr/.ipynb_checkpoints/GPR_TBL4_DOI_Resolution-checkpoint.ipynb b/Notebooks/gpr/.ipynb_checkpoints/GPR_TBL4_DOI_Resolution-checkpoint.ipynb
similarity index 100%
rename from gpr/.ipynb_checkpoints/GPR_TBL4_DOI_Resolution-checkpoint.ipynb
rename to Notebooks/gpr/.ipynb_checkpoints/GPR_TBL4_DOI_Resolution-checkpoint.ipynb
diff --git a/gpr/GPR_Attenuation.ipynb b/Notebooks/gpr/GPR_Attenuation.ipynb
similarity index 90%
rename from gpr/GPR_Attenuation.ipynb
rename to Notebooks/gpr/GPR_Attenuation.ipynb
index 9dcd6bf8d84a0e7554d93889d49b56f90d269a93..57e133d953b4817826338b1475249d69e8ad717c 100644
--- a/gpr/GPR_Attenuation.ipynb
+++ b/Notebooks/gpr/GPR_Attenuation.ipynb
@@ -57,13 +57,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33e25b12a4174c048ea647727c6b586e",
+       "model_id": "dc3b14b7fccc4f4f88914e5b5aad3a3d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -73,6 +73,16 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function geoscilabs.gpr.Attenuation.WaveVelandSkindWidgetTBL(epsr, log_sigma, log_frequency)>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
diff --git a/gpr/GPR_Lab6_FitData.ipynb b/Notebooks/gpr/GPR_Lab6_FitData.ipynb
similarity index 88%
rename from gpr/GPR_Lab6_FitData.ipynb
rename to Notebooks/gpr/GPR_Lab6_FitData.ipynb
index 0f10a42e6c895d4d28c55039ad31c1b8351c0627..eb235d5d53136f58e72e0c03fe827ffc713720e2 100644
--- a/gpr/GPR_Lab6_FitData.ipynb
+++ b/Notebooks/gpr/GPR_Lab6_FitData.ipynb
@@ -34,7 +34,10 @@
    "source": [
     "%matplotlib inline\n",
     "from geoscilabs.gpr.GPRlab1 import downloadRadargramImage, PipeWidget, WallWidget\n",
-    "from SimPEG.utils import download"
+    "from SimPEG.utils import download\n",
+    "\n",
+    "import os\n",
+    "from PIL import Image"
    ]
   },
   {
@@ -63,7 +66,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "11c9969799104002bacd4dd8b11d872e",
+       "model_id": "5114d488ae354db19842dc1c412e0567",
        "version_major": 2,
        "version_minor": 0
       },
@@ -86,8 +89,12 @@
     }
    ],
    "source": [
-    "URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
-    "radargramImage = downloadRadargramImage(URL)\n",
+    "#URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
+    "URL = os.getcwd()+ \"/ubc_GPRdata.png\"\n",
+    "\n",
+    "#radargramImage = downloadRadargramImage(URL)\n",
+    "radargramImage = Image.open(URL)\n",
+    "\n",
     "PipeWidget(radargramImage)"
    ]
   },
@@ -119,7 +126,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe04a72dc0fa4fdab47146e0f44aeaef",
+       "model_id": "0e40457b4ed34dc58b654d19c9725c60",
        "version_major": 2,
        "version_minor": 0
       },
@@ -142,8 +149,12 @@
     }
    ],
    "source": [
-    "URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
-    "radargramImage = downloadRadargramImage(URL)\n",
+    "#URL = \"http://github.com/geoscixyz/geosci-labs/raw/main/images/gpr/ubc_GPRdata.png\"\n",
+    "#radargramImage = downloadRadargramImage(URL)\n",
+    "\n",
+    "URL = os.getcwd()+ \"/ubc_GPRdata.png\"\n",
+    "radargramImage = Image.open(URL)\n",
+    "\n",
     "WallWidget(radargramImage)"
    ]
   },
diff --git a/gpr/GPR_TBL4_DOI_Resolution.ipynb b/Notebooks/gpr/GPR_TBL4_DOI_Resolution.ipynb
similarity index 100%
rename from gpr/GPR_TBL4_DOI_Resolution.ipynb
rename to Notebooks/gpr/GPR_TBL4_DOI_Resolution.ipynb
diff --git a/Notebooks/gpr/ubc_GPRdata.png b/Notebooks/gpr/ubc_GPRdata.png
new file mode 100644
index 0000000000000000000000000000000000000000..ebc5cdefa2be26c06c451a4f8caec1dcc187d5ee
Binary files /dev/null and b/Notebooks/gpr/ubc_GPRdata.png differ
diff --git a/Notebooks/gravity/.ipynb_checkpoints/gravityDike-checkpoint.ipynb b/Notebooks/gravity/.ipynb_checkpoints/gravityDike-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cdc63c4697642a9fb82156da86280647ca5894bd
--- /dev/null
+++ b/Notebooks/gravity/.ipynb_checkpoints/gravityDike-checkpoint.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gravity anomaly from a dike\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a 2D dipping dike that is infinite along one horizontal direction and buried in the subsurface.\n",
+    "\n",
+    "Three plots are produced:\n",
+    "\n",
+    "(1) Data measured along a profile on the surface across the dike\n",
+    "\n",
+    "(2) Cross-sectional diagram of the dike model\n",
+    "\n",
+    "(3) Plan view of data measured over a grid on the surface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from geoscilabs.gravity.gravityDike import interact_gravity_Dike, printResult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The following parameters are adjustable.\n",
+    "\n",
+    "$\\Delta \\rho$: Density contrast defined as the density of the dike minus the density of the host rock (in $g/cm^3$)\n",
+    "\n",
+    "$z_1$: Depth to the top of the dike (in $m$)\n",
+    "\n",
+    "$z_2$: Depth to the bottom of the dike (in $m$)\n",
+    "\n",
+    "$b$: Horizontal width of the dike (in $m$)\n",
+    "\n",
+    "$\\beta$: Dipping angle of the dike from vertical (in $degree$)\n",
+    "\n",
+    "$Step$: Spacing between measurements on the surface (in $m$)\n",
+    "\n",
+    "\n",
+    "The vertical component of the gravity anomaly at a point on the surface can be calculated as\n",
+    "\n",
+    "$\\Delta g_z = 2 G \\Delta \\rho [ z_2 (\\theta_2-\\theta_4) - z1 (\\theta_1-\\theta_3) + \\mathbf{sin}\\beta \\mathbf{cos}\\beta \\{x (\\theta_2-\\theta_1) - (x-b) (\\theta_4-\\theta_3)\\} + \\mathbf{cos}^2\\beta \\{x \\mathbf{ln}(r_2/r_1) - (x-b) \\mathbf{ln}(r_4/r_3) \\} ]$ in mGal,\n",
+    "\n",
+    "where $G = 6.674 \\times 10^{-8} cm^3 \\cdot g^{-1} \\cdot s^{-2}$ is the gravititional constant and $\\theta$'s are in radian.\n",
+    "\n",
+    "<img style=\"width: 800px\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/gravity/gravityDike.png?raw=true\">\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b349a68808bd4d87a6946dd3fd350bd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(VBox(children=(FloatSlider(value=1.0, continuous_update=False, description='$\\\\D…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "interact_gravity_Dike()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print data\n",
+    "\n",
+    "Run the cell below to print the measurement locations and the measured data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    X        Δgz    \n",
+      "  -6.000   0.000352 \n",
+      "  -5.879   0.000368 \n",
+      "  -5.758   0.000384 \n",
+      "  -5.636   0.000402 \n",
+      "  -5.515   0.000421 \n",
+      "  -5.394   0.000441 \n",
+      "  -5.273   0.000463 \n",
+      "  -5.152   0.000487 \n",
+      "  -5.030   0.000512 \n",
+      "  -4.909   0.000539 \n",
+      "  -4.788   0.000568 \n",
+      "  -4.667   0.000600 \n",
+      "  -4.545   0.000635 \n",
+      "  -4.424   0.000672 \n",
+      "  -4.303   0.000713 \n",
+      "  -4.182   0.000758 \n",
+      "  -4.061   0.000806 \n",
+      "  -3.939   0.000860 \n",
+      "  -3.818   0.000919 \n",
+      "  -3.697   0.000984 \n",
+      "  -3.576   0.001056 \n",
+      "  -3.455   0.001135 \n",
+      "  -3.333   0.001224 \n",
+      "  -3.212   0.001323 \n",
+      "  -3.091   0.001433 \n",
+      "  -2.970   0.001557 \n",
+      "  -2.848   0.001697 \n",
+      "  -2.727   0.001855 \n",
+      "  -2.606   0.002034 \n",
+      "  -2.485   0.002238 \n",
+      "  -2.364   0.002470 \n",
+      "  -2.242   0.002735 \n",
+      "  -2.121   0.003038 \n",
+      "  -2.000   0.003385 \n",
+      "  -1.879   0.003782 \n",
+      "  -1.758   0.004236 \n",
+      "  -1.636   0.004754 \n",
+      "  -1.515   0.005342 \n",
+      "  -1.394   0.006005 \n",
+      "  -1.273   0.006748 \n",
+      "  -1.152   0.007571 \n",
+      "  -1.030   0.008474 \n",
+      "  -0.909   0.009450 \n",
+      "  -0.788   0.010484 \n",
+      "  -0.667   0.011550 \n",
+      "  -0.545   0.012597 \n",
+      "  -0.424   0.013536 \n",
+      "  -0.303   0.014234 \n",
+      "  -0.182   0.014569 \n",
+      "  -0.061   0.014493 \n",
+      "  0.061    0.014028 \n",
+      "  0.182    0.013223 \n",
+      "  0.303    0.012135 \n",
+      "  0.424    0.010830 \n",
+      "  0.545    0.009400 \n",
+      "  0.667    0.007977 \n",
+      "  0.788    0.006697 \n",
+      "  0.909    0.005635 \n",
+      "  1.030    0.004785 \n",
+      "  1.152    0.004109 \n",
+      "  1.273    0.003565 \n",
+      "  1.394    0.003123 \n",
+      "  1.515    0.002759 \n",
+      "  1.636    0.002455 \n",
+      "  1.758    0.002199 \n",
+      "  1.879    0.001982 \n",
+      "  2.000    0.001795 \n",
+      "  2.121    0.001633 \n",
+      "  2.242    0.001493 \n",
+      "  2.364    0.001369 \n",
+      "  2.485    0.001261 \n",
+      "  2.606    0.001165 \n",
+      "  2.727    0.001079 \n",
+      "  2.848    0.001003 \n",
+      "  2.970    0.000934 \n",
+      "  3.091    0.000872 \n",
+      "  3.212    0.000816 \n",
+      "  3.333    0.000766 \n",
+      "  3.455    0.000720 \n",
+      "  3.576    0.000677 \n",
+      "  3.697    0.000639 \n",
+      "  3.818    0.000604 \n",
+      "  3.939    0.000571 \n",
+      "  4.061    0.000541 \n",
+      "  4.182    0.000513 \n",
+      "  4.303    0.000488 \n",
+      "  4.424    0.000464 \n",
+      "  4.545    0.000442 \n",
+      "  4.667    0.000422 \n",
+      "  4.788    0.000402 \n",
+      "  4.909    0.000385 \n",
+      "  5.030    0.000368 \n",
+      "  5.152    0.000352 \n",
+      "  5.273    0.000338 \n",
+      "  5.394    0.000324 \n",
+      "  5.515    0.000311 \n",
+      "  5.636    0.000299 \n",
+      "  5.758    0.000287 \n",
+      "  5.879    0.000276 \n",
+      "  6.000    0.000266 \n"
+     ]
+    }
+   ],
+   "source": [
+    "printResult()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/gravity/.ipynb_checkpoints/gravitySphere-checkpoint.ipynb b/Notebooks/gravity/.ipynb_checkpoints/gravitySphere-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2b26fe4d3364c0c4fe40bf666bd6a19fbda191a3
--- /dev/null
+++ b/Notebooks/gravity/.ipynb_checkpoints/gravitySphere-checkpoint.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gravity anomaly from a sphere\n",
+    "\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a sphere buried in the subsurface.\n",
+    "\n",
+    "Three plots are produced:\n",
+    "\n",
+    "(1) Data measured along a profile on the surface across the sphere\n",
+    "\n",
+    "(2) Cross-sectional diagram of the sphere model\n",
+    "\n",
+    "(3) Plan view of data measured over a grid on the surface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from geoscilabs.gravity.gravitySphere import interact_gravity_sphere, printResult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The following parameters are adjustable.\n",
+    "\n",
+    "$\\Delta \\rho$: Density contrast defined as the density of the dike minus the density of the host rock (in $g/cm^3$)\n",
+    "\n",
+    "$a$: Radius of the sphere (in $m$)\n",
+    "\n",
+    "$z$: Depth to the center of the sphere (in $m$)\n",
+    "\n",
+    "$Step$: Spacing between measurements on the surface (in $m$)\n",
+    "\n",
+    "\n",
+    "The vertical component of the gravity anomaly at a point on the surface can be calculated as\n",
+    "\n",
+    "$\\Delta g_z = G \\Delta \\rho a^3 4/3 z / r^3$ in mGal,\n",
+    "\n",
+    "where $G = 6.674 \\times 10^{-8} cm^3 \\cdot g^{-1} \\cdot s^{-2}$ is the gravititional constant and $r = \\sqrt{x^2+z^2}$ is the distance between the sphere's center and the observation point on the surface.\n",
+    "\n",
+    "<img style=\"width: 600px\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/gravity/gravitySphere.png?raw=true\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a720d923881d477688f37268d185d582",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='$\\\\Delta\\\\rho$', max=5.0, m…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "interact_gravity_sphere()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print data\n",
+    "\n",
+    "Run the cell below to print the measurement locations and the measured data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    X        Δgz    \n",
+      " -10.000   0.000028 \n",
+      "  -9.798   0.000029 \n",
+      "  -9.596   0.000031 \n",
+      "  -9.394   0.000033 \n",
+      "  -9.192   0.000035 \n",
+      "  -8.990   0.000038 \n",
+      "  -8.788   0.000040 \n",
+      "  -8.586   0.000043 \n",
+      "  -8.384   0.000046 \n",
+      "  -8.182   0.000050 \n",
+      "  -7.980   0.000054 \n",
+      "  -7.778   0.000058 \n",
+      "  -7.576   0.000063 \n",
+      "  -7.374   0.000068 \n",
+      "  -7.172   0.000074 \n",
+      "  -6.970   0.000080 \n",
+      "  -6.768   0.000087 \n",
+      "  -6.566   0.000095 \n",
+      "  -6.364   0.000105 \n",
+      "  -6.162   0.000115 \n",
+      "  -5.960   0.000127 \n",
+      "  -5.758   0.000140 \n",
+      "  -5.556   0.000155 \n",
+      "  -5.354   0.000173 \n",
+      "  -5.152   0.000193 \n",
+      "  -4.949   0.000217 \n",
+      "  -4.747   0.000245 \n",
+      "  -4.545   0.000277 \n",
+      "  -4.343   0.000316 \n",
+      "  -4.141   0.000362 \n",
+      "  -3.939   0.000416 \n",
+      "  -3.737   0.000483 \n",
+      "  -3.535   0.000564 \n",
+      "  -3.333   0.000663 \n",
+      "  -3.131   0.000787 \n",
+      "  -2.929   0.000943 \n",
+      "  -2.727   0.001141 \n",
+      "  -2.525   0.001395 \n",
+      "  -2.323   0.001728 \n",
+      "  -2.121   0.002168 \n",
+      "  -1.919   0.002758 \n",
+      "  -1.717   0.003563 \n",
+      "  -1.515   0.004673 \n",
+      "  -1.313   0.006217 \n",
+      "  -1.111   0.008370 \n",
+      "  -0.909   0.011326 \n",
+      "  -0.707   0.015219 \n",
+      "  -0.505   0.019883 \n",
+      "  -0.303   0.024505 \n",
+      "  -0.101   0.027535 \n",
+      "  0.101    0.027535 \n",
+      "  0.303    0.024505 \n",
+      "  0.505    0.019883 \n",
+      "  0.707    0.015219 \n",
+      "  0.909    0.011326 \n",
+      "  1.111    0.008370 \n",
+      "  1.313    0.006217 \n",
+      "  1.515    0.004673 \n",
+      "  1.717    0.003563 \n",
+      "  1.919    0.002758 \n",
+      "  2.121    0.002168 \n",
+      "  2.323    0.001728 \n",
+      "  2.525    0.001395 \n",
+      "  2.727    0.001141 \n",
+      "  2.929    0.000943 \n",
+      "  3.131    0.000787 \n",
+      "  3.333    0.000663 \n",
+      "  3.535    0.000564 \n",
+      "  3.737    0.000483 \n",
+      "  3.939    0.000416 \n",
+      "  4.141    0.000362 \n",
+      "  4.343    0.000316 \n",
+      "  4.545    0.000277 \n",
+      "  4.747    0.000245 \n",
+      "  4.949    0.000217 \n",
+      "  5.152    0.000193 \n",
+      "  5.354    0.000173 \n",
+      "  5.556    0.000155 \n",
+      "  5.758    0.000140 \n",
+      "  5.960    0.000127 \n",
+      "  6.162    0.000115 \n",
+      "  6.364    0.000105 \n",
+      "  6.566    0.000095 \n",
+      "  6.768    0.000087 \n",
+      "  6.970    0.000080 \n",
+      "  7.172    0.000074 \n",
+      "  7.374    0.000068 \n",
+      "  7.576    0.000063 \n",
+      "  7.778    0.000058 \n",
+      "  7.980    0.000054 \n",
+      "  8.182    0.000050 \n",
+      "  8.384    0.000046 \n",
+      "  8.586    0.000043 \n",
+      "  8.788    0.000040 \n",
+      "  8.990    0.000038 \n",
+      "  9.192    0.000035 \n",
+      "  9.394    0.000033 \n",
+      "  9.596    0.000031 \n",
+      "  9.798    0.000029 \n",
+      "  10.000   0.000028 \n"
+     ]
+    }
+   ],
+   "source": [
+    "printResult()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/gravity/gravityDike.ipynb b/Notebooks/gravity/gravityDike.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..11e16d3bde46627587e00357632f1393df2faa4e
--- /dev/null
+++ b/Notebooks/gravity/gravityDike.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gravity anomaly from a dike\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a 2D dipping dike that is infinite along one horizontal direction and buried in the subsurface.\n",
+    "\n",
+    "Three plots are produced:\n",
+    "\n",
+    "(1) Data measured along a profile on the surface across the dike\n",
+    "\n",
+    "(2) Cross-sectional diagram of the dike model\n",
+    "\n",
+    "(3) Plan view of data measured over a grid on the surface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from geoscilabs.gravity.gravityDike import interact_gravity_Dike, printResult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The following parameters are adjustable.\n",
+    "\n",
+    "$\\Delta \\rho$: Density contrast defined as the density of the dike minus the density of the host rock (in $g/cm^3$)\n",
+    "\n",
+    "$z_1$: Depth to the top of the dike (in $m$)\n",
+    "\n",
+    "$z_2$: Depth to the bottom of the dike (in $m$)\n",
+    "\n",
+    "$b$: Horizontal width of the dike (in $m$)\n",
+    "\n",
+    "$\\beta$: Dipping angle of the dike from vertical (in $degree$)\n",
+    "\n",
+    "$Step$: Spacing between measurements on the surface (in $m$)\n",
+    "\n",
+    "\n",
+    "The vertical component of the gravity anomaly at a point on the surface can be calculated as\n",
+    "\n",
+    "$\\Delta g_z = 2 G \\Delta \\rho [ z_2 (\\theta_2-\\theta_4) - z1 (\\theta_1-\\theta_3) + \\mathbf{sin}\\beta \\mathbf{cos}\\beta \\{x (\\theta_2-\\theta_1) - (x-b) (\\theta_4-\\theta_3)\\} + \\mathbf{cos}^2\\beta \\{x \\mathbf{ln}(r_2/r_1) - (x-b) \\mathbf{ln}(r_4/r_3) \\} ]$ in mGal,\n",
+    "\n",
+    "where $G = 6.674 \\times 10^{-8} cm^3 \\cdot g^{-1} \\cdot s^{-2}$ is the gravititional constant and $\\theta$'s are in radian.\n",
+    "\n",
+    "<img style=\"width: 800px\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/gravity/gravityDike.png?raw=true\">\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c80473d5a22404daaeeb7f5f2c5439b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(VBox(children=(FloatSlider(value=1.0, continuous_update=False, description='$\\\\D…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "interact_gravity_Dike()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print data\n",
+    "\n",
+    "Run the cell below to print the measurement locations and the measured data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    X        Δgz    \n",
+      "  -6.000   0.000352 \n",
+      "  -5.879   0.000368 \n",
+      "  -5.758   0.000384 \n",
+      "  -5.636   0.000402 \n",
+      "  -5.515   0.000421 \n",
+      "  -5.394   0.000441 \n",
+      "  -5.273   0.000463 \n",
+      "  -5.152   0.000487 \n",
+      "  -5.030   0.000512 \n",
+      "  -4.909   0.000539 \n",
+      "  -4.788   0.000568 \n",
+      "  -4.667   0.000600 \n",
+      "  -4.545   0.000635 \n",
+      "  -4.424   0.000672 \n",
+      "  -4.303   0.000713 \n",
+      "  -4.182   0.000758 \n",
+      "  -4.061   0.000806 \n",
+      "  -3.939   0.000860 \n",
+      "  -3.818   0.000919 \n",
+      "  -3.697   0.000984 \n",
+      "  -3.576   0.001056 \n",
+      "  -3.455   0.001135 \n",
+      "  -3.333   0.001224 \n",
+      "  -3.212   0.001323 \n",
+      "  -3.091   0.001433 \n",
+      "  -2.970   0.001557 \n",
+      "  -2.848   0.001697 \n",
+      "  -2.727   0.001855 \n",
+      "  -2.606   0.002034 \n",
+      "  -2.485   0.002238 \n",
+      "  -2.364   0.002470 \n",
+      "  -2.242   0.002735 \n",
+      "  -2.121   0.003038 \n",
+      "  -2.000   0.003385 \n",
+      "  -1.879   0.003782 \n",
+      "  -1.758   0.004236 \n",
+      "  -1.636   0.004754 \n",
+      "  -1.515   0.005342 \n",
+      "  -1.394   0.006005 \n",
+      "  -1.273   0.006748 \n",
+      "  -1.152   0.007571 \n",
+      "  -1.030   0.008474 \n",
+      "  -0.909   0.009450 \n",
+      "  -0.788   0.010484 \n",
+      "  -0.667   0.011550 \n",
+      "  -0.545   0.012597 \n",
+      "  -0.424   0.013536 \n",
+      "  -0.303   0.014234 \n",
+      "  -0.182   0.014569 \n",
+      "  -0.061   0.014493 \n",
+      "  0.061    0.014028 \n",
+      "  0.182    0.013223 \n",
+      "  0.303    0.012135 \n",
+      "  0.424    0.010830 \n",
+      "  0.545    0.009400 \n",
+      "  0.667    0.007977 \n",
+      "  0.788    0.006697 \n",
+      "  0.909    0.005635 \n",
+      "  1.030    0.004785 \n",
+      "  1.152    0.004109 \n",
+      "  1.273    0.003565 \n",
+      "  1.394    0.003123 \n",
+      "  1.515    0.002759 \n",
+      "  1.636    0.002455 \n",
+      "  1.758    0.002199 \n",
+      "  1.879    0.001982 \n",
+      "  2.000    0.001795 \n",
+      "  2.121    0.001633 \n",
+      "  2.242    0.001493 \n",
+      "  2.364    0.001369 \n",
+      "  2.485    0.001261 \n",
+      "  2.606    0.001165 \n",
+      "  2.727    0.001079 \n",
+      "  2.848    0.001003 \n",
+      "  2.970    0.000934 \n",
+      "  3.091    0.000872 \n",
+      "  3.212    0.000816 \n",
+      "  3.333    0.000766 \n",
+      "  3.455    0.000720 \n",
+      "  3.576    0.000677 \n",
+      "  3.697    0.000639 \n",
+      "  3.818    0.000604 \n",
+      "  3.939    0.000571 \n",
+      "  4.061    0.000541 \n",
+      "  4.182    0.000513 \n",
+      "  4.303    0.000488 \n",
+      "  4.424    0.000464 \n",
+      "  4.545    0.000442 \n",
+      "  4.667    0.000422 \n",
+      "  4.788    0.000402 \n",
+      "  4.909    0.000385 \n",
+      "  5.030    0.000368 \n",
+      "  5.152    0.000352 \n",
+      "  5.273    0.000338 \n",
+      "  5.394    0.000324 \n",
+      "  5.515    0.000311 \n",
+      "  5.636    0.000299 \n",
+      "  5.758    0.000287 \n",
+      "  5.879    0.000276 \n",
+      "  6.000    0.000266 \n"
+     ]
+    }
+   ],
+   "source": [
+    "printResult()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/gravity/gravitySphere.ipynb b/Notebooks/gravity/gravitySphere.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d90106da0f5d7b4c6322314892d872ca5ed4d582
--- /dev/null
+++ b/Notebooks/gravity/gravitySphere.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gravity anomaly from a sphere\n",
+    "\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a sphere buried in the subsurface.\n",
+    "\n",
+    "Three plots are produced:\n",
+    "\n",
+    "(1) Data measured along a profile on the surface across the sphere\n",
+    "\n",
+    "(2) Cross-sectional diagram of the sphere model\n",
+    "\n",
+    "(3) Plan view of data measured over a grid on the surface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from geoscilabs.gravity.gravitySphere import interact_gravity_sphere, printResult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The following parameters are adjustable.\n",
+    "\n",
+    "$\\Delta \\rho$: Density contrast defined as the density of the dike minus the density of the host rock (in $g/cm^3$)\n",
+    "\n",
+    "$a$: Radius of the sphere (in $m$)\n",
+    "\n",
+    "$z$: Depth to the center of the sphere (in $m$)\n",
+    "\n",
+    "$Step$: Spacing between measurements on the surface (in $m$)\n",
+    "\n",
+    "\n",
+    "The vertical component of the gravity anomaly at a point on the surface can be calculated as\n",
+    "\n",
+    "$\\Delta g_z = G \\Delta \\rho a^3 4/3 z / r^3$ in mGal,\n",
+    "\n",
+    "where $G = 6.674 \\times 10^{-8} cm^3 \\cdot g^{-1} \\cdot s^{-2}$ is the gravititional constant and $r = \\sqrt{x^2+z^2}$ is the distance between the sphere's center and the observation point on the surface.\n",
+    "\n",
+    "<img style=\"width: 600px\" src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/gravity/gravitySphere.png?raw=true\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "929948c8163f4508bbaf98cd5eced47f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='$\\\\Delta\\\\rho$', max=5.0, m…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "interact_gravity_sphere()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print data\n",
+    "\n",
+    "Run the cell below to print the measurement locations and the measured data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    X        Δgz    \n",
+      " -10.000   0.000028 \n",
+      "  -9.798   0.000029 \n",
+      "  -9.596   0.000031 \n",
+      "  -9.394   0.000033 \n",
+      "  -9.192   0.000035 \n",
+      "  -8.990   0.000038 \n",
+      "  -8.788   0.000040 \n",
+      "  -8.586   0.000043 \n",
+      "  -8.384   0.000046 \n",
+      "  -8.182   0.000050 \n",
+      "  -7.980   0.000054 \n",
+      "  -7.778   0.000058 \n",
+      "  -7.576   0.000063 \n",
+      "  -7.374   0.000068 \n",
+      "  -7.172   0.000074 \n",
+      "  -6.970   0.000080 \n",
+      "  -6.768   0.000087 \n",
+      "  -6.566   0.000095 \n",
+      "  -6.364   0.000105 \n",
+      "  -6.162   0.000115 \n",
+      "  -5.960   0.000127 \n",
+      "  -5.758   0.000140 \n",
+      "  -5.556   0.000155 \n",
+      "  -5.354   0.000173 \n",
+      "  -5.152   0.000193 \n",
+      "  -4.949   0.000217 \n",
+      "  -4.747   0.000245 \n",
+      "  -4.545   0.000277 \n",
+      "  -4.343   0.000316 \n",
+      "  -4.141   0.000362 \n",
+      "  -3.939   0.000416 \n",
+      "  -3.737   0.000483 \n",
+      "  -3.535   0.000564 \n",
+      "  -3.333   0.000663 \n",
+      "  -3.131   0.000787 \n",
+      "  -2.929   0.000943 \n",
+      "  -2.727   0.001141 \n",
+      "  -2.525   0.001395 \n",
+      "  -2.323   0.001728 \n",
+      "  -2.121   0.002168 \n",
+      "  -1.919   0.002758 \n",
+      "  -1.717   0.003563 \n",
+      "  -1.515   0.004673 \n",
+      "  -1.313   0.006217 \n",
+      "  -1.111   0.008370 \n",
+      "  -0.909   0.011326 \n",
+      "  -0.707   0.015219 \n",
+      "  -0.505   0.019883 \n",
+      "  -0.303   0.024505 \n",
+      "  -0.101   0.027535 \n",
+      "  0.101    0.027535 \n",
+      "  0.303    0.024505 \n",
+      "  0.505    0.019883 \n",
+      "  0.707    0.015219 \n",
+      "  0.909    0.011326 \n",
+      "  1.111    0.008370 \n",
+      "  1.313    0.006217 \n",
+      "  1.515    0.004673 \n",
+      "  1.717    0.003563 \n",
+      "  1.919    0.002758 \n",
+      "  2.121    0.002168 \n",
+      "  2.323    0.001728 \n",
+      "  2.525    0.001395 \n",
+      "  2.727    0.001141 \n",
+      "  2.929    0.000943 \n",
+      "  3.131    0.000787 \n",
+      "  3.333    0.000663 \n",
+      "  3.535    0.000564 \n",
+      "  3.737    0.000483 \n",
+      "  3.939    0.000416 \n",
+      "  4.141    0.000362 \n",
+      "  4.343    0.000316 \n",
+      "  4.545    0.000277 \n",
+      "  4.747    0.000245 \n",
+      "  4.949    0.000217 \n",
+      "  5.152    0.000193 \n",
+      "  5.354    0.000173 \n",
+      "  5.556    0.000155 \n",
+      "  5.758    0.000140 \n",
+      "  5.960    0.000127 \n",
+      "  6.162    0.000115 \n",
+      "  6.364    0.000105 \n",
+      "  6.566    0.000095 \n",
+      "  6.768    0.000087 \n",
+      "  6.970    0.000080 \n",
+      "  7.172    0.000074 \n",
+      "  7.374    0.000068 \n",
+      "  7.576    0.000063 \n",
+      "  7.778    0.000058 \n",
+      "  7.980    0.000054 \n",
+      "  8.182    0.000050 \n",
+      "  8.384    0.000046 \n",
+      "  8.586    0.000043 \n",
+      "  8.788    0.000040 \n",
+      "  8.990    0.000038 \n",
+      "  9.192    0.000035 \n",
+      "  9.394    0.000033 \n",
+      "  9.596    0.000031 \n",
+      "  9.798    0.000029 \n",
+      "  10.000   0.000028 \n"
+     ]
+    }
+   ],
+   "source": [
+    "printResult()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/mag/.ipynb_checkpoints/Mag_Dipole-checkpoint.ipynb b/Notebooks/mag/.ipynb_checkpoints/Mag_Dipole-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8f5ae134814c6ff250d398aa8f867e9ef3816255
--- /dev/null
+++ b/Notebooks/mag/.ipynb_checkpoints/Mag_Dipole-checkpoint.ipynb
@@ -0,0 +1,1201 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from geoscilabs.mag.MagDipole import MagneticLongDipoleLine, MagneticLongDipoleField"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a magnetic dipole\n",
+    "\n",
+    "A dipole is defined in the section below using\n",
+    "* dipoleloc: x, y, z location of the dipole center\n",
+    "* dipoledec: declination of the dipole's direction in degree; north = 0; positive clockwise\n",
+    "* dipoleinc: declination of the dipole's direction in degree; horizontal = 0; positive down\n",
+    "* dipoleL: length of the dipole *L* or the distance between two opposite charges *Q* that make the dipole\n",
+    "* dipolemoement: $m=\\frac{QL}{\\mu_0}$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a dipole\n",
+    "dipoleloc = (0.,0.,-50.)\n",
+    "dipoleL = 100.\n",
+    "dipoledec, dipoleinc = 0., 90.\n",
+    "dipolemoment = 1e13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the Earth's magnetic field $B_0$\n",
+    "$B_0$ is used to calcualte the total field anomaly, which is the projection of the anomalous vector field onto the earth's field (inner product).\n",
+    "* B0: the magnitude of the earth's field\n",
+    "* Binc: inclination of the earth's field\n",
+    "* Bdec: declination of the earth's field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# geomagnetic field\n",
+    "B0, Binc, Bdec = 53600e-9, 90., 0. # in Tesla, degree, degree\n",
+    "B0x = B0*np.cos(np.radians(Binc))*np.sin(np.radians(Bdec))\n",
+    "B0y = B0*np.cos(np.radians(Binc))*np.cos(np.radians(Bdec))\n",
+    "B0z = -B0*np.sin(np.radians(Binc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the observations\n",
+    "Four data plots will be generated in the figure: magnetic anomalous field map (contour) at a certain elevation, magnetic anomalous field data (curve) along a x-profile and a y-profile, and the magnetic field lines of the dipole. \n",
+    "* xmin, xmax, ymin, ymax: the outer bounds of the survey grid\n",
+    "* z: elevation at which the data map is measured\n",
+    "* profile_x, profile_y: x-coordinate of y-profile, y-coordinate of x-profile\n",
+    "* h: grid interval\n",
+    "* radii: how far the field lines expand; can plot multiple layers if given (r1, r2, ...)\n",
+    "* Naz: number of azimuth angles for the field line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set observation grid\n",
+    "xmin, xmax, ymin, ymax, z = -5., 5., -5., 5., 1. # x, y bounds and elevation\n",
+    "profile_x = 0. # x-coordinate of y-profile\n",
+    "profile_y = 0. # y-coordinate of x-profile\n",
+    "h = 0.2 # grid interval\n",
+    "radii = (2., 5.) # how many layers of field lines for plotting\n",
+    "Naz = 10 # number of azimuth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate data for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get field lines\n",
+    "linex, liney, linez = MagneticLongDipoleLine(dipoleloc,dipoledec,dipoleinc,dipoleL,radii,Naz)\n",
+    "\n",
+    "# get map\n",
+    "xi, yi = np.meshgrid(np.r_[xmin:xmax+h:h], np.r_[ymin:ymax+h:h])\n",
+    "x1, y1 = xi.flatten(), yi.flatten()\n",
+    "z1 = np.full(x1.shape,z)\n",
+    "Bx, By, Bz = np.zeros(len(x1)), np.zeros(len(x1)), np.zeros(len(x1))\n",
+    "\n",
+    "for i in np.arange(len(x1)):\n",
+    "    Bx[i], By[i], Bz[i] = MagneticLongDipoleField(dipoleloc,dipoledec,dipoleinc,dipoleL,(x1[i],y1[i],z1[i]),dipolemoment)\n",
+    "Ba1 = np.dot(np.r_[B0x,B0y,B0z], np.vstack((Bx,By,Bz)))\n",
+    "\n",
+    "# get x-profile\n",
+    "x2 = np.r_[xmin:xmax+h:h]\n",
+    "y2, z2 = np.full(x2.shape,profile_y), np.full(x2.shape,z)\n",
+    "Bx, By, Bz = np.zeros(len(x2)), np.zeros(len(x2)), np.zeros(len(x2))\n",
+    "for i in np.arange(len(x2)):\n",
+    "    Bx[i], By[i], Bz[i] = MagneticLongDipoleField(dipoleloc,dipoledec,dipoleinc,dipoleL,(x2[i],y2[i],z2[i]),dipolemoment)\n",
+    "Ba2 = np.dot(np.r_[B0x,B0y,B0z], np.vstack((Bx,By,Bz)))\n",
+    "\n",
+    "# get y-profile\n",
+    "y3 = np.r_[ymin:ymax+h:h]\n",
+    "x3, z3 = np.full(y3.shape,profile_x), np.full(y3.shape,z)\n",
+    "Bx, By, Bz = np.zeros(len(x3)), np.zeros(len(x3)), np.zeros(len(x3))\n",
+    "for i in np.arange(len(x3)):\n",
+    "    Bx[i], By[i], Bz[i] = MagneticLongDipoleField(dipoleloc,dipoledec,dipoleinc,dipoleL,(x3[i],y3[i],z3[i]),dipolemoment)\n",
+    "Ba3 = np.dot(np.r_[B0x,B0y,B0z], np.vstack((Bx,By,Bz)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3D plot of field lines and data\n",
+    "* Color bar in nT\n",
+    "* Spatial distance in meter\n",
+    "* Profile data only reflects shape of anomaly and positivity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "/* global mpl */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function () {\n",
+       "    if (typeof WebSocket !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert(\n",
+       "            'Your browser does not have WebSocket support. ' +\n",
+       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "                'Firefox 4 and 5 are also supported but you ' +\n",
+       "                'have to enable WebSockets in about:config.'\n",
+       "        );\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById('mpl-warnings');\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent =\n",
+       "                'This browser does not support binary websocket messages. ' +\n",
+       "                'Performance may be slow.';\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = document.createElement('div');\n",
+       "    this.root.setAttribute('style', 'display: inline-block');\n",
+       "    this._root_extra_style(this.root);\n",
+       "\n",
+       "    parent_element.appendChild(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen = function () {\n",
+       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+       "        fig.send_message('send_image_mode', {});\n",
+       "        if (fig.ratio !== 1) {\n",
+       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
+       "        }\n",
+       "        fig.send_message('refresh', {});\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onload = function () {\n",
+       "        if (fig.image_mode === 'full') {\n",
+       "            // Full images could contain transparency (where diff images\n",
+       "            // almost always do), so we need to clear the canvas so that\n",
+       "            // there is no ghosting.\n",
+       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "        }\n",
+       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onunload = function () {\n",
+       "        fig.ws.close();\n",
+       "    };\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function () {\n",
+       "    var titlebar = document.createElement('div');\n",
+       "    titlebar.classList =\n",
+       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+       "    var titletext = document.createElement('div');\n",
+       "    titletext.classList = 'ui-dialog-title';\n",
+       "    titletext.setAttribute(\n",
+       "        'style',\n",
+       "        'width: 100%; text-align: center; padding: 3px;'\n",
+       "    );\n",
+       "    titlebar.appendChild(titletext);\n",
+       "    this.root.appendChild(titlebar);\n",
+       "    this.header = titletext;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+       "    canvas_div.setAttribute(\n",
+       "        'style',\n",
+       "        'border: 1px solid #ddd;' +\n",
+       "            'box-sizing: content-box;' +\n",
+       "            'clear: both;' +\n",
+       "            'min-height: 1px;' +\n",
+       "            'min-width: 1px;' +\n",
+       "            'outline: 0;' +\n",
+       "            'overflow: hidden;' +\n",
+       "            'position: relative;' +\n",
+       "            'resize: both;'\n",
+       "    );\n",
+       "\n",
+       "    function on_keyboard_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.key_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keydown',\n",
+       "        on_keyboard_event_closure('key_press')\n",
+       "    );\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keyup',\n",
+       "        on_keyboard_event_closure('key_release')\n",
+       "    );\n",
+       "\n",
+       "    this._canvas_extra_style(canvas_div);\n",
+       "    this.root.appendChild(canvas_div);\n",
+       "\n",
+       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
+       "    canvas.classList.add('mpl-canvas');\n",
+       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+       "\n",
+       "    this.context = canvas.getContext('2d');\n",
+       "\n",
+       "    var backingStore =\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        this.context.webkitBackingStorePixelRatio ||\n",
+       "        this.context.mozBackingStorePixelRatio ||\n",
+       "        this.context.msBackingStorePixelRatio ||\n",
+       "        this.context.oBackingStorePixelRatio ||\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        1;\n",
+       "\n",
+       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+       "        'canvas'\n",
+       "    ));\n",
+       "    rubberband_canvas.setAttribute(\n",
+       "        'style',\n",
+       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+       "    );\n",
+       "\n",
+       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+       "    if (this.ResizeObserver === undefined) {\n",
+       "        if (window.ResizeObserver !== undefined) {\n",
+       "            this.ResizeObserver = window.ResizeObserver;\n",
+       "        } else {\n",
+       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+       "            this.ResizeObserver = obs.ResizeObserver;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+       "        var nentries = entries.length;\n",
+       "        for (var i = 0; i < nentries; i++) {\n",
+       "            var entry = entries[i];\n",
+       "            var width, height;\n",
+       "            if (entry.contentBoxSize) {\n",
+       "                if (entry.contentBoxSize instanceof Array) {\n",
+       "                    // Chrome 84 implements new version of spec.\n",
+       "                    width = entry.contentBoxSize[0].inlineSize;\n",
+       "                    height = entry.contentBoxSize[0].blockSize;\n",
+       "                } else {\n",
+       "                    // Firefox implements old version of spec.\n",
+       "                    width = entry.contentBoxSize.inlineSize;\n",
+       "                    height = entry.contentBoxSize.blockSize;\n",
+       "                }\n",
+       "            } else {\n",
+       "                // Chrome <84 implements even older version of spec.\n",
+       "                width = entry.contentRect.width;\n",
+       "                height = entry.contentRect.height;\n",
+       "            }\n",
+       "\n",
+       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
+       "            // the canvas container.\n",
+       "            if (entry.devicePixelContentBoxSize) {\n",
+       "                // Chrome 84 implements new version of spec.\n",
+       "                canvas.setAttribute(\n",
+       "                    'width',\n",
+       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
+       "                );\n",
+       "                canvas.setAttribute(\n",
+       "                    'height',\n",
+       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
+       "                );\n",
+       "            } else {\n",
+       "                canvas.setAttribute('width', width * fig.ratio);\n",
+       "                canvas.setAttribute('height', height * fig.ratio);\n",
+       "            }\n",
+       "            canvas.setAttribute(\n",
+       "                'style',\n",
+       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
+       "            );\n",
+       "\n",
+       "            rubberband_canvas.setAttribute('width', width);\n",
+       "            rubberband_canvas.setAttribute('height', height);\n",
+       "\n",
+       "            // And update the size in Python. We ignore the initial 0/0 size\n",
+       "            // that occurs as the element is placed into the DOM, which should\n",
+       "            // otherwise not happen due to the minimum size styling.\n",
+       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+       "                fig.request_resize(width, height);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    this.resizeObserverInstance.observe(canvas_div);\n",
+       "\n",
+       "    function on_mouse_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.mouse_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousedown',\n",
+       "        on_mouse_event_closure('button_press')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseup',\n",
+       "        on_mouse_event_closure('button_release')\n",
+       "    );\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousemove',\n",
+       "        on_mouse_event_closure('motion_notify')\n",
+       "    );\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseenter',\n",
+       "        on_mouse_event_closure('figure_enter')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseleave',\n",
+       "        on_mouse_event_closure('figure_leave')\n",
+       "    );\n",
+       "\n",
+       "    canvas_div.addEventListener('wheel', function (event) {\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        on_mouse_event_closure('scroll')(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.appendChild(canvas);\n",
+       "    canvas_div.appendChild(rubberband_canvas);\n",
+       "\n",
+       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+       "    this.rubberband_context.strokeStyle = '#000000';\n",
+       "\n",
+       "    this._resize_canvas = function (width, height, forward) {\n",
+       "        if (forward) {\n",
+       "            canvas_div.style.width = width + 'px';\n",
+       "            canvas_div.style.height = height + 'px';\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+       "        event.preventDefault();\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus() {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'mpl-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'mpl-button-group';\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'mpl-button-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
+       "        button.classList = 'mpl-widget';\n",
+       "        button.setAttribute('role', 'button');\n",
+       "        button.setAttribute('aria-disabled', 'false');\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "\n",
+       "        var icon_img = document.createElement('img');\n",
+       "        icon_img.src = '_images/' + image + '.png';\n",
+       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+       "        icon_img.alt = tooltip;\n",
+       "        button.appendChild(icon_img);\n",
+       "\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker = document.createElement('select');\n",
+       "    fmt_picker.classList = 'mpl-widget';\n",
+       "    toolbar.appendChild(fmt_picker);\n",
+       "    this.format_dropdown = fmt_picker;\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = document.createElement('option');\n",
+       "        option.selected = fmt === mpl.default_extension;\n",
+       "        option.innerHTML = fmt;\n",
+       "        fmt_picker.appendChild(option);\n",
+       "    }\n",
+       "\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function (type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function () {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+       "        fig.send_message('refresh', {});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+       "    var x0 = msg['x0'] / fig.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+       "    var x1 = msg['x1'] / fig.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0,\n",
+       "        0,\n",
+       "        fig.canvas.width / fig.ratio,\n",
+       "        fig.canvas.height / fig.ratio\n",
+       "    );\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch (cursor) {\n",
+       "        case 0:\n",
+       "            cursor = 'pointer';\n",
+       "            break;\n",
+       "        case 1:\n",
+       "            cursor = 'default';\n",
+       "            break;\n",
+       "        case 2:\n",
+       "            cursor = 'crosshair';\n",
+       "            break;\n",
+       "        case 3:\n",
+       "            cursor = 'move';\n",
+       "            break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+       "    for (var key in msg) {\n",
+       "        if (!(key in fig.buttons)) {\n",
+       "            continue;\n",
+       "        }\n",
+       "        fig.buttons[key].disabled = !msg[key];\n",
+       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+       "    if (msg['mode'] === 'PAN') {\n",
+       "        fig.buttons['Pan'].classList.add('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    } else if (msg['mode'] === 'ZOOM') {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.add('active');\n",
+       "    } else {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message('ack', {});\n",
+       "};\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = 'image/png';\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src\n",
+       "                );\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data\n",
+       "            );\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        } else if (\n",
+       "            typeof evt.data === 'string' &&\n",
+       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+       "        ) {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig['handle_' + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\n",
+       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
+       "                msg\n",
+       "            );\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\n",
+       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+       "                    e,\n",
+       "                    e.stack,\n",
+       "                    msg\n",
+       "                );\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function (e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e) {\n",
+       "        e = window.event;\n",
+       "    }\n",
+       "    if (e.target) {\n",
+       "        targ = e.target;\n",
+       "    } else if (e.srcElement) {\n",
+       "        targ = e.srcElement;\n",
+       "    }\n",
+       "    if (targ.nodeType === 3) {\n",
+       "        // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "    }\n",
+       "\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    var boundingRect = targ.getBoundingClientRect();\n",
+       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+       "\n",
+       "    return { x: x, y: y };\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys(original) {\n",
+       "    return Object.keys(original).reduce(function (obj, key) {\n",
+       "        if (typeof original[key] !== 'object') {\n",
+       "            obj[key] = original[key];\n",
+       "        }\n",
+       "        return obj;\n",
+       "    }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event);\n",
+       "\n",
+       "    if (name === 'button_press') {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * this.ratio;\n",
+       "    var y = canvas_pos.y * this.ratio;\n",
+       "\n",
+       "    this.send_message(name, {\n",
+       "        x: x,\n",
+       "        y: y,\n",
+       "        button: event.button,\n",
+       "        step: event.step,\n",
+       "        guiEvent: simpleKeys(event),\n",
+       "    });\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function (event, name) {\n",
+       "    // Prevent repeat events\n",
+       "    if (name === 'key_press') {\n",
+       "        if (event.which === this._key) {\n",
+       "            return;\n",
+       "        } else {\n",
+       "            this._key = event.which;\n",
+       "        }\n",
+       "    }\n",
+       "    if (name === 'key_release') {\n",
+       "        this._key = null;\n",
+       "    }\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which !== 17) {\n",
+       "        value += 'ctrl+';\n",
+       "    }\n",
+       "    if (event.altKey && event.which !== 18) {\n",
+       "        value += 'alt+';\n",
+       "    }\n",
+       "    if (event.shiftKey && event.which !== 16) {\n",
+       "        value += 'shift+';\n",
+       "    }\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+       "    if (name === 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message('toolbar_button', { name: name });\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "\n",
+       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+       "// prettier-ignore\n",
+       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";/* global mpl */\n",
+       "\n",
+       "var comm_websocket_adapter = function (comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function () {\n",
+       "        comm.close();\n",
+       "    };\n",
+       "    ws.send = function (m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function (msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data']);\n",
+       "    });\n",
+       "    return ws;\n",
+       "};\n",
+       "\n",
+       "mpl.mpl_figure_comm = function (comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = document.getElementById(id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm);\n",
+       "\n",
+       "    function ondownload(figure, _format) {\n",
+       "        window.open(figure.canvas.toDataURL());\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element;\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error('Failed to find cell for figure', id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.cell_info[0].output_area.element.on(\n",
+       "        'cleared',\n",
+       "        { fig: fig },\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+       "    var width = fig.canvas.width / fig.ratio;\n",
+       "    fig.cell_info[0].output_area.element.off(\n",
+       "        'cleared',\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable();\n",
+       "    fig.parent_element.innerHTML =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "    fig.close_ws(fig, msg);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width / this.ratio;\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message('ack', {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () {\n",
+       "        fig.push_to_output();\n",
+       "    }, 1000);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'btn-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'btn-group';\n",
+       "    var button;\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'btn-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        button = fig.buttons[name] = document.createElement('button');\n",
+       "        button.classList = 'btn btn-default';\n",
+       "        button.href = '#';\n",
+       "        button.title = name;\n",
+       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message pull-right';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = document.createElement('div');\n",
+       "    buttongrp.classList = 'btn-group inline pull-right';\n",
+       "    button = document.createElement('button');\n",
+       "    button.classList = 'btn btn-mini btn-primary';\n",
+       "    button.href = '#';\n",
+       "    button.title = 'Stop Interaction';\n",
+       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+       "    button.addEventListener('click', function (_evt) {\n",
+       "        fig.handle_close(fig, {});\n",
+       "    });\n",
+       "    button.addEventListener(\n",
+       "        'mouseover',\n",
+       "        on_mouseover_closure('Stop Interaction')\n",
+       "    );\n",
+       "    buttongrp.appendChild(button);\n",
+       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+       "    var fig = event.data.fig;\n",
+       "    if (event.target !== this) {\n",
+       "        // Ignore bubbled events from children.\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.close_ws(fig, {});\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (el) {\n",
+       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.setAttribute('tabindex', 0);\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    } else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager) {\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "    }\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which === 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "};\n",
+       "\n",
+       "mpl.find_output_cell = function (html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i = 0; i < ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code') {\n",
+       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] === html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel !== null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target(\n",
+       "        'matplotlib',\n",
+       "        mpl.mpl_figure_comm\n",
+       "    );\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-9.0, 7.5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca(projection='3d')\n",
+    "\n",
+    "# plot field lines\n",
+    "for lx,ly,lz in zip(linex,liney,linez):\n",
+    "    ax.plot(lx,ly,lz,'-',markersize=1)\n",
+    "\n",
+    "# plot map\n",
+    "ax.scatter(x1,y1,z1,s=2,alpha=0.3)\n",
+    "Bt = Ba1.reshape(xi.shape)*1e9 # contour and color scale in nT \n",
+    "c = ax.contourf(xi,yi,Bt,alpha=1,zdir='z',offset=z-max(radii)*2,cmap='jet',\n",
+    "                  levels=np.linspace(Bt.min(),Bt.max(),50,endpoint=True))\n",
+    "fig.colorbar(c)\n",
+    "\n",
+    "# auto-scaling for profile plot\n",
+    "ptpmax = np.max((Ba2.ptp(),Ba3.ptp())) # dynamic range\n",
+    "autoscaling = np.max(radii) / ptpmax\n",
+    "\n",
+    "# plot x-profile\n",
+    "ax.scatter(x2,y2,z2,s=2,c='black',alpha=0.3)\n",
+    "ax.plot(x2,Ba2*autoscaling,zs=ymax,c='black',zdir='y')\n",
+    "\n",
+    "# plot y-profile\n",
+    "ax.scatter(x3,y3,z3,s=2,c='black',alpha=0.3)\n",
+    "ax.plot(y3,Ba3*autoscaling,zs=xmin,c='black',zdir='x')\n",
+    "\n",
+    "ax.set_xlabel('X')\n",
+    "ax.set_ylabel('Y')\n",
+    "ax.set_zlabel('Z')\n",
+    "\n",
+    "ax.set_xlim(xmin, xmax)\n",
+    "ax.set_ylim(ymin, ymax)\n",
+    "ax.set_zlim(z-max(radii)*2, max(radii)*1.5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/mag/.ipynb_checkpoints/Mag_FitProfile-checkpoint.ipynb b/Notebooks/mag/.ipynb_checkpoints/Mag_FitProfile-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..319e2064a26d2697d9701e838d1933a34566a210
--- /dev/null
+++ b/Notebooks/mag/.ipynb_checkpoints/Mag_FitProfile-checkpoint.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 10,
+        "hidden": false,
+        "row": 0,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) \n",
+    "\n",
+    "This notebook uses code adapted from \n",
+    "\n",
+    "SimPEG\n",
+    "- Cockett, R., S. Kang, L.J. Heagy, A. Pidlisecky, D.W. Oldenburg (2015, in review), SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers and Geosciences\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 10,
+        "width": 6
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "## View the model\n",
+    "\n",
+    "- dx: width or prism in x-direction (m)\n",
+    "- dy: width of prism in y-direction (m)\n",
+    "- dz: vertical extent of prism (m)\n",
+    "- x0: x location of the center of the prism (m)\n",
+    "- y0: y location of the center of the prism (m)\n",
+    "- depth: depth to the top of the prism (m)\n",
+    "- prism_inc: inclination of the prism (reference is a unit northing vector; degrees)\n",
+    "- prism_dec: declination of the prism (reference is a unit northing vector; degrees)\n",
+    "- View_dip: dip angle of view (degrees)\n",
+    "- View_elev: elevation of view (degrees)\n",
+    "- View_azim: azimuth of view (degrees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 29,
+        "hidden": false,
+        "row": 21,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from geoscilabs.mag import Mag, Simulator\n",
+    "from discretize import TensorMesh\n",
+    "from SimPEG import utils\n",
+    "from SimPEG.potential_fields import magnetics as mag\n",
+    "from ipywidgets import widgets\n",
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from SimPEG.utils import download\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bbf8705d2a4626bbe0658fcd3a6416",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='Refresh'), FloatSlider(value=2.375, continuous_up…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Input parameters\n",
+    "\n",
+    "#fileName = 'http://github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/Lab1_Wednesday_TA.csv'\n",
+    "#synData  = download(fileName,overwrite=True,verbose=False)\n",
+    "\n",
+    "fileName = os.getcwd()+ \"/Lab1_Wednesday_TA.csv\"\n",
+    "\n",
+    "data = np.genfromtxt(fileName, skip_header=1, delimiter=',')\n",
+    "xyzd = np.c_[np.zeros(data.shape[0]), data[:,0], np.zeros(data.shape[0]), data[:,1]]\n",
+    "B = np.r_[60308, 83.8, 25.4]\n",
+    "survey, dobj = Mag.createMagSurvey(xyzd, B)\n",
+    "# View the data and chose a profile\n",
+    "# Define the parametric model interactively\n",
+    "model = Simulator.ViewPrism(survey)\n",
+    "display(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit the data\n",
+    "- Binc: Inclination of the Earth's background field (degree)\n",
+    "- Bdec: Declination of the Earth's background field (degree)\n",
+    "- Bigrf: Strength of the Earth's background field (nT) \n",
+    "- depth: vertical distance from the sensor to the top of the rebar (m)\n",
+    "- susc: magnetic susceptibility\n",
+    "- comp: Total field (tf) of component of the field to plot\n",
+    "- irt: Type of magnetization \n",
+    "- Q: Koenigsberger ratio ($\\frac{M_{rem}}{M_{ind}}$)\n",
+    "- rinc: inclination of the remanent magnetization (degree)\n",
+    "- rdec: declination of the remanent magnetization (degree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 6,
+        "height": 29,
+        "hidden": false,
+        "row": 21,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "012534ac73914e05a943624e10942b80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=90.0, continuous_update=False, description='Binc', max=90.0, min=-90.0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q = Simulator.fitline(model,survey,dobj)\n",
+    "display(Q)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "grid_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "8789ea09a28443d0b196412593e51195": {
+     "views": [
+      {
+       "cell_index": 3
+      }
+     ]
+    },
+    "8bd53575b8d6470cb6158b113159182e": {
+     "views": [
+      {
+       "cell_index": 5
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/mag/.ipynb_checkpoints/Mag_Induced2D-checkpoint.ipynb b/Notebooks/mag/.ipynb_checkpoints/Mag_Induced2D-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a348ba91f2463c06f0869e30a9db0efd2bf58344
--- /dev/null
+++ b/Notebooks/mag/.ipynb_checkpoints/Mag_Induced2D-checkpoint.ipynb
@@ -0,0 +1,419 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 0,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) \n",
+    "\n",
+    "This notebook uses code adapted from \n",
+    "\n",
+    "SimPEG\n",
+    "- Cockett, R., S. Kang, L.J. Heagy, A. Pidlisecky, D.W. Oldenburg (2015, in review), SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers and Geosciences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 3,
+        "hidden": true,
+        "row": 11,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from geoscilabs.mag import Mag, Simulator\n",
+    "from SimPEG.potential_fields import magnetics as mag\n",
+    "from SimPEG import utils, data\n",
+    "from discretize import TensorMesh\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 21,
+        "hidden": false,
+        "row": 22,
+        "width": null
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# How do we define direction of an earth magnetic field?\n",
+    "\n",
+    "Earth magnetic field is a vector. To define a vector we need to choose a coordinate system. We use right-handed system: \n",
+    "- X (Easting), \n",
+    "- Y (Northing), and \n",
+    "- Z (Up). \n",
+    "\n",
+    "Here we consider an earth magnetic field ($\\vec{B_0}$), of which intensity is one. To define this unit vector, we use inclinatino and declination:\n",
+    "- Declination: An angle from geographic North (Ng) (positive clockwise)\n",
+    "- Inclination: Vertical angle from the N-E plane (positive down)\n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/raw/main/images/mag/earthfield.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 18,
+        "hidden": false,
+        "row": 43,
+        "width": null
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# What's data: total field anomaly\n",
+    "\n",
+    "We consider a typical form of magnetic data. To illustrate this we consider an suceptible object embedded in the earth. \n",
+    "Based upon the earth magnetic field ($\\vec{B}_0$), this object will generate anomalous magnetic field ($\\vec{B}_A$). We define an unit vector $\\hat{B}_0$ for the earth field as \n",
+    "$$ \\hat{B}_0 = \\frac{\\vec{B}_0}{|\\vec{B}_0|}$$ \n",
+    "We measure both earth and anomalous magnetic field such that\n",
+    "\n",
+    "$$ \\vec{B} = \\vec{B}_0 + \\vec{B}_A$$\n",
+    "\n",
+    "Total field anomaly, $\\triangle \\vec{B}$ can be defined as\n",
+    "\n",
+    "$$  |\\triangle \\vec{B}| = |\\vec{B}|-|\\vec{B}_E| $$ \n",
+    "\n",
+    "If $|\\vec{B}|\\ll|\\vec{B}_E|$, then that is total field anomaly $\\triangle \\vec{B}$ is the projection of the anomalous field onto the direction of the earth field:\n",
+    "\n",
+    "$$ |\\triangle \\vec{B}| \\simeq \\vec{B}_A \\cdot \\hat{B}_0=|\\vec{B}_A|cos\\theta$$ \n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/raw/main/images/mag/totalfieldanomaly.png?raw=true\" style=\"width: 50%; height: 50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 11,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# Define a 3D prism\n",
+    "Our model is a rectangular prism. Parameters to define this prism are given below:\n",
+    "\n",
+    "- dx: length in Easting (x) direction (meter)\n",
+    "- dy: length in Northing (y) direction (meter)\n",
+    "- dz: length in Depth (z) direction (meter) below the receiver\n",
+    "- depth: top boundary of the prism (meter)\n",
+    "- pinc: inclination of the prism (reference is a unit northing vector; degree)\n",
+    "- pdec: declination of the prism (reference is a unit northing vector; degree)\n",
+    "\n",
+    "You can also change the height of the survey grid above the ground\n",
+    "- rx_h: height of the grid (meter)\n",
+    "\n",
+    "*Green dots show a plane where we measure data.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 28,
+        "hidden": false,
+        "row": 61,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82d6267941c242a3acd81042ed90a234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=557366.1875, continuous_update=False, description='East', max=558589.8…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Input parameters\n",
+    "\n",
+    "#fileName = 'https://github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/DO27_TMI.dat'\n",
+    "\n",
+    "fileName = os.getcwd()+ \"/DO27_TMI.dat\"\n",
+    "\n",
+    "\n",
+    "xyzd = np.genfromtxt(fileName, skip_header=3)\n",
+    "B = np.r_[60308, 83.8, 25.4]\n",
+    "survey, dobj = Mag.createMagSurvey(xyzd, B)\n",
+    "# View the data and chose a profile\n",
+    "param = Simulator.ViewMagSurvey2D(survey, dobj)\n",
+    "display(param)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a98e5f0cd294c96a2f78c89ea44ac08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='Refresh'), FloatSlider(value=152.953125, continuo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the parametric model interactively\n",
+    "model = Simulator.ViewPrism(param.result)\n",
+    "display(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 6,
+        "height": 11,
+        "hidden": false,
+        "row": 11,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# Magnetic applet\n",
+    "Based on the prism that you made above, below Magnetic applet computes magnetic field at receiver locations, and provide both 2D map (left) and profile line (right). \n",
+    "\n",
+    "For the prism, you can alter:\n",
+    "- sus: susceptibility of the prism\n",
+    "\n",
+    "Parameters for the earth field are:\n",
+    "- Einc: inclination of the earth field (degree)\n",
+    "- Edec: declination of the earth field (degree)\n",
+    "- Bigrf: intensity of the earth field (nT)\n",
+    "\n",
+    "For data, you can view:\n",
+    "- tf: total field anomaly,  \n",
+    "- bx :x-component, \n",
+    "- by :y-component, \n",
+    "- bz :z-component\n",
+    "\n",
+    "You can simulate and view remanent magnetization effect with parameters:\n",
+    "- irt: \"induced\", \"remanent\", or \"total\"\n",
+    "- Q: Koenigsberger ratio ($\\frac{M_{rem}}{M_{ind}}$)\n",
+    "- rinc: inclination of the remanent magnetization (degree)\n",
+    "- rdec: declination of the remanent magnetization (degree)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 6,
+        "height": 28,
+        "hidden": false,
+        "row": 61,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c43af169fe743a9832809433addb4c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='Refresh'), FloatSlider(value=0.1, continuous_upda…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotwidget = Simulator.PFSimulator(model, param)\n",
+    "display(plotwidget)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "report_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "2c77e5c891dd44069234331d87475ef2": {
+     "views": [
+      {
+       "cell_index": 5
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/mag/.ipynb_checkpoints/MagneticDipoleApplet-checkpoint.ipynb b/Notebooks/mag/.ipynb_checkpoints/MagneticDipoleApplet-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b47fe04580c156ffdb3c2a7538ab7dc478c4f4cf
--- /dev/null
+++ b/Notebooks/mag/.ipynb_checkpoints/MagneticDipoleApplet-checkpoint.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.mag.MagDipoleApp import MagneticDipoleApp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Magnetic Dipole Applet\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a small buried dipolar magnet. In geophysics, this simulates the observed anomaly over a buried susceptible sphere that is magnetized by the Earth's magnetic field.\n",
+    "\n",
+    "## What is shown\n",
+    "\n",
+    "- <b>The colour map</b> shows the strength of the chosen parameter (Bt, Bx, By, Bz, or Bg) as a function of position.\n",
+    "\n",
+    "- Imagine doing a two dimensional survey over a susceptible sphere that has been magentized by the Earth's magnetic field specified by inclination and declination.  \"Measurement\" location is the centre of each coloured box. This is a simple (but easily programmable) alternative to generating a smooth contour map.\n",
+    "\n",
+    "- The anomaly depends upon magnetic latitude, direction of the inducing (Earth's) field, the depth of the buried dipole, and the magnetic moment of the buried dipole.\n",
+    "\n",
+    "\n",
+    "## Important Notes:\n",
+    "\n",
+    "- <b>Inclination (I)</b> and <b>declination (D)</b> describe the orientation of the Earth's ambient field at the centre of the survey area. Positive inclination implies you are in the northern hemisphere, and positive declination implies that magnetic north is to the east of geographic north.\n",
+    "\n",
+    "- The <b>\"length\"</b> adjuster changes the size of the square survey area. The default of 72 means the survey square is 72 metres on a side.\n",
+    "\n",
+    "- The <b>\"data spacing\"</b> adjuster changes the distance between measurements. The default of 1 means measurements were acquired over the survey square on a 2-metre grid. In other words, \"data spacing = 2\" means each coloured box is 2 m square.\n",
+    "\n",
+    "- The <b>\"depth\"</b> adjuster changes the depth (in metres) to the centre of the buried dipole.\n",
+    "\n",
+    "- The <b>\"magnetic moment (M)\"</b> adjuster changes the strength of the induced field. Units are Am2.  This is related to the strength of the inducing field, the susceptibility of the buried sphere, and the volume of susceptible material.\n",
+    "- <b>Bt, Bg, Bx, By, Bz</b> are Total field, X-component (positive northwards), Y-component (positive eastwards), and Z-component (positive down) of the anomaly field respectively.\n",
+    "\n",
+    "- Checking the <b>fixed scale</b> button fixes the colour scale so that the end points of the colour scale are minimum and maximum values for the current data set.\n",
+    "\n",
+    "- You can generate a <b>profile</b> along either \"East\" or \"North\" direction\n",
+    "\n",
+    "- Check <b>half width</b> to see the half width of the anomaly. Anomaly width is noted on the botton of the graph.\n",
+    "\n",
+    "- Measurements are taken 1m above the surface.\n",
+    "\n",
+    "- For gradient data (<b>Bg</b>), measurements are taken at 1m and 2m\n",
+    "\n",
+    "- Note that magnetic moment (M) for monopole is equal to the charge (Q): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8add133a23c4749a81f269ec705d44f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(RadioButtons(description='field', options=('Bt', 'Bx', 'By', 'Bz', 'Bg'), value=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag = MagneticDipoleApp()\n",
+    "mag.interact_plot_model_dipole()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two monopoles (pseudo dipole)\n",
+    "\n",
+    "Different from the previous app, here we focus on a situtation where we have to monopoles having negative\n",
+    "and postive signs. Their horizontal location: (X, Y) are same, but depths are different. By default depth of the negative pole, <b>depth$_{-Q}$ </b>, is located 0m and that of the positive pole, <b>depth$_{-Q}$ </b>, is 10m. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e22ddaaa4fb439eb9e967b48d81568f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(RadioButtons(description='field', options=('Bt', 'Bx', 'By', 'Bz', 'Bg'), value=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag.interact_plot_model_two_monopole()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/mag/.ipynb_checkpoints/MagneticPrismApplet-checkpoint.ipynb b/Notebooks/mag/.ipynb_checkpoints/MagneticPrismApplet-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cf568dcd894c70fe9cca2f3da1e347f63be168d7
--- /dev/null
+++ b/Notebooks/mag/.ipynb_checkpoints/MagneticPrismApplet-checkpoint.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 0,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.mag.MagDipoleApp import MagneticDipoleApp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Magnetic Prism Applet\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 3,
+        "hidden": true,
+        "row": 11,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "From the Magnetic Dipole applet, we have learned how anomalous magnetic field observed at ground's surface look\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a retangular susceptible prism. \n",
+    "\n",
+    "\n",
+    "## What is shown\n",
+    "\n",
+    "- <b>The colour map</b> shows the strength of the chosen parameter (Bt, Bx, By, or Bz) as a function of position.\n",
+    "\n",
+    "- Imagine doing a two dimensional survey over a susceptible sphere that has been magentized by the Earth's magnetic field specified by inclination and declination.  \"Measurement\" location is the centre of each coloured box. This is a simple (but easily programmable) alternative to generating a smooth contour map.\n",
+    "\n",
+    "- The anomaly depends upon magnetic latitude, direction of the inducing (Earth's) field, the depth of the buried dipole, and the magnetic moment of the buried dipole.\n",
+    "\n",
+    "\n",
+    "## Important Notes:\n",
+    "\n",
+    "- <b>Inclination (I)</b> and <b>declination (D)</b> describe the orientation of the Earth's ambient field at the centre of the survey area. Positive inclination implies you are in the northern hemisphere, and positive declination implies that magnetic north is to the east of geographic north.\n",
+    "\n",
+    "- The <b>\"length\"</b> adjuster changes the size of the square survey area. The default of 72 means the survey square is 72 metres on a side.\n",
+    "\n",
+    "- The <b>\"data spacing\"</b> adjuster changes the distance between measurements. The default of 1 means measurements were acquired over the survey square on a 2-metre grid. In other words, \"data spacing = 2\" means each coloured box is 2 m square.\n",
+    "\n",
+    "- The <b>\"depth\"</b> adjuster changes the depth (in metres) to the top of the buried prism.\n",
+    "\n",
+    "- The <b>\"magnetic moment (M)\"</b> adjuster changes the strength of the induced field. Units are Am2.  This is related to the strength of the inducing field, the susceptibility of the buried sphere, and the volume of susceptible material.\n",
+    "- <b>Bt, Bx, By, Bz</b> are Total field, X-component (positive northwards), Y-component (positive eastwards), and Z-component (positive down) of the anomaly field respectively.\n",
+    "\n",
+    "- Checking the <b>fixed scale</b> button fixes the colour scale so that the end points of the colour scale are minimum and maximum values for the current data set.\n",
+    "\n",
+    "- You can generate a <b>profile</b> along either \"East\" or \"North\" direction\n",
+    "\n",
+    "- Check <b>half width</b> to see the half width of the anomaly. Anomaly width is noted on the botton of the graph.\n",
+    "\n",
+    "- Measurements are taken 1m above the surface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 11,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "## Define a 3D prism\n",
+    "Compared to the MagneticDipoleApplet, there are additional parameters to define a prism.\n",
+    "\n",
+    "- $\\triangle x$: length in North (X) direction (m)\n",
+    "- $\\triangle y$: length in East (Y) direction (m)\n",
+    "- $\\triangle z$: length in Depth (z) direction (m) below the receiver\n",
+    "- depth: top boundary of the prism (meter)\n",
+    "- I$_{prism}$: inclination of the prism (reference is north direction)\n",
+    "- D$_{prism}$: declination of the prism (reference is north direction) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64d0ad8c47ad4681b4bbb15180cb6042",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(RadioButtons(description='plot', options=('field', 'model'), value='field'), Rad…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag = MagneticDipoleApp()\n",
+    "mag.interact_plot_model_prism()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "report_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "2c77e5c891dd44069234331d87475ef2": {
+     "views": [
+      {
+       "cell_index": 5
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/mag/DO27_TMI.dat b/Notebooks/mag/DO27_TMI.dat
new file mode 100644
index 0000000000000000000000000000000000000000..ea4d95c3a7ac408f6e077563d44b3b817b426ac5
--- /dev/null
+++ b/Notebooks/mag/DO27_TMI.dat
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diff --git a/Notebooks/mag/Lab1_Wednesday_TA.csv b/Notebooks/mag/Lab1_Wednesday_TA.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ebc9df34171d3a6ade4ad25cecc6cb05b15a946a
--- /dev/null
+++ b/Notebooks/mag/Lab1_Wednesday_TA.csv
@@ -0,0 +1,24 @@
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diff --git a/Notebooks/mag/Mag_Dipole.ipynb b/Notebooks/mag/Mag_Dipole.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..76db5aeea37264db94947d5677eeb28515727a22
--- /dev/null
+++ b/Notebooks/mag/Mag_Dipole.ipynb
@@ -0,0 +1,1201 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from geoscilabs.mag.MagDipole import MagneticLongDipoleLine, MagneticLongDipoleField"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a magnetic dipole\n",
+    "\n",
+    "A dipole is defined in the section below using\n",
+    "* dipoleloc: x, y, z location of the dipole center\n",
+    "* dipoledec: declination of the dipole's direction in degree; north = 0; positive clockwise\n",
+    "* dipoleinc: declination of the dipole's direction in degree; horizontal = 0; positive down\n",
+    "* dipoleL: length of the dipole *L* or the distance between two opposite charges *Q* that make the dipole\n",
+    "* dipolemoement: $m=\\frac{QL}{\\mu_0}$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a dipole\n",
+    "dipoleloc = (0.,0.,-50.)\n",
+    "dipoleL = 100.\n",
+    "dipoledec, dipoleinc = 0., 90.\n",
+    "dipolemoment = 1e13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the Earth's magnetic field $B_0$\n",
+    "$B_0$ is used to calcualte the total field anomaly, which is the projection of the anomalous vector field onto the earth's field (inner product).\n",
+    "* B0: the magnitude of the earth's field\n",
+    "* Binc: inclination of the earth's field\n",
+    "* Bdec: declination of the earth's field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# geomagnetic field\n",
+    "B0, Binc, Bdec = 53600e-9, 90., 0. # in Tesla, degree, degree\n",
+    "B0x = B0*np.cos(np.radians(Binc))*np.sin(np.radians(Bdec))\n",
+    "B0y = B0*np.cos(np.radians(Binc))*np.cos(np.radians(Bdec))\n",
+    "B0z = -B0*np.sin(np.radians(Binc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the observations\n",
+    "Four data plots will be generated in the figure: magnetic anomalous field map (contour) at a certain elevation, magnetic anomalous field data (curve) along a x-profile and a y-profile, and the magnetic field lines of the dipole. \n",
+    "* xmin, xmax, ymin, ymax: the outer bounds of the survey grid\n",
+    "* z: elevation at which the data map is measured\n",
+    "* profile_x, profile_y: x-coordinate of y-profile, y-coordinate of x-profile\n",
+    "* h: grid interval\n",
+    "* radii: how far the field lines expand; can plot multiple layers if given (r1, r2, ...)\n",
+    "* Naz: number of azimuth angles for the field line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set observation grid\n",
+    "xmin, xmax, ymin, ymax, z = -5., 5., -5., 5., 1. # x, y bounds and elevation\n",
+    "profile_x = 0. # x-coordinate of y-profile\n",
+    "profile_y = 0. # y-coordinate of x-profile\n",
+    "h = 0.2 # grid interval\n",
+    "radii = (2., 5.) # how many layers of field lines for plotting\n",
+    "Naz = 10 # number of azimuth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate data for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get field lines\n",
+    "linex, liney, linez = MagneticLongDipoleLine(dipoleloc,dipoledec,dipoleinc,dipoleL,radii,Naz)\n",
+    "\n",
+    "# get map\n",
+    "xi, yi = np.meshgrid(np.r_[xmin:xmax+h:h], np.r_[ymin:ymax+h:h])\n",
+    "x1, y1 = xi.flatten(), yi.flatten()\n",
+    "z1 = np.full(x1.shape,z)\n",
+    "Bx, By, Bz = np.zeros(len(x1)), np.zeros(len(x1)), np.zeros(len(x1))\n",
+    "\n",
+    "for i in np.arange(len(x1)):\n",
+    "    Bx[i], By[i], Bz[i] = MagneticLongDipoleField(dipoleloc,dipoledec,dipoleinc,dipoleL,(x1[i],y1[i],z1[i]),dipolemoment)\n",
+    "Ba1 = np.dot(np.r_[B0x,B0y,B0z], np.vstack((Bx,By,Bz)))\n",
+    "\n",
+    "# get x-profile\n",
+    "x2 = np.r_[xmin:xmax+h:h]\n",
+    "y2, z2 = np.full(x2.shape,profile_y), np.full(x2.shape,z)\n",
+    "Bx, By, Bz = np.zeros(len(x2)), np.zeros(len(x2)), np.zeros(len(x2))\n",
+    "for i in np.arange(len(x2)):\n",
+    "    Bx[i], By[i], Bz[i] = MagneticLongDipoleField(dipoleloc,dipoledec,dipoleinc,dipoleL,(x2[i],y2[i],z2[i]),dipolemoment)\n",
+    "Ba2 = np.dot(np.r_[B0x,B0y,B0z], np.vstack((Bx,By,Bz)))\n",
+    "\n",
+    "# get y-profile\n",
+    "y3 = np.r_[ymin:ymax+h:h]\n",
+    "x3, z3 = np.full(y3.shape,profile_x), np.full(y3.shape,z)\n",
+    "Bx, By, Bz = np.zeros(len(x3)), np.zeros(len(x3)), np.zeros(len(x3))\n",
+    "for i in np.arange(len(x3)):\n",
+    "    Bx[i], By[i], Bz[i] = MagneticLongDipoleField(dipoleloc,dipoledec,dipoleinc,dipoleL,(x3[i],y3[i],z3[i]),dipolemoment)\n",
+    "Ba3 = np.dot(np.r_[B0x,B0y,B0z], np.vstack((Bx,By,Bz)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3D plot of field lines and data\n",
+    "* Color bar in nT\n",
+    "* Spatial distance in meter\n",
+    "* Profile data only reflects shape of anomaly and positivity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
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+       "\n",
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+       "    titletext.setAttribute(\n",
+       "        'style',\n",
+       "        'width: 100%; text-align: center; padding: 3px;'\n",
+       "    );\n",
+       "    titlebar.appendChild(titletext);\n",
+       "    this.root.appendChild(titlebar);\n",
+       "    this.header = titletext;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+       "    canvas_div.setAttribute(\n",
+       "        'style',\n",
+       "        'border: 1px solid #ddd;' +\n",
+       "            'box-sizing: content-box;' +\n",
+       "            'clear: both;' +\n",
+       "            'min-height: 1px;' +\n",
+       "            'min-width: 1px;' +\n",
+       "            'outline: 0;' +\n",
+       "            'overflow: hidden;' +\n",
+       "            'position: relative;' +\n",
+       "            'resize: both;'\n",
+       "    );\n",
+       "\n",
+       "    function on_keyboard_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.key_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keydown',\n",
+       "        on_keyboard_event_closure('key_press')\n",
+       "    );\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keyup',\n",
+       "        on_keyboard_event_closure('key_release')\n",
+       "    );\n",
+       "\n",
+       "    this._canvas_extra_style(canvas_div);\n",
+       "    this.root.appendChild(canvas_div);\n",
+       "\n",
+       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
+       "    canvas.classList.add('mpl-canvas');\n",
+       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+       "\n",
+       "    this.context = canvas.getContext('2d');\n",
+       "\n",
+       "    var backingStore =\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        this.context.webkitBackingStorePixelRatio ||\n",
+       "        this.context.mozBackingStorePixelRatio ||\n",
+       "        this.context.msBackingStorePixelRatio ||\n",
+       "        this.context.oBackingStorePixelRatio ||\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        1;\n",
+       "\n",
+       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+       "        'canvas'\n",
+       "    ));\n",
+       "    rubberband_canvas.setAttribute(\n",
+       "        'style',\n",
+       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+       "    );\n",
+       "\n",
+       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+       "    if (this.ResizeObserver === undefined) {\n",
+       "        if (window.ResizeObserver !== undefined) {\n",
+       "            this.ResizeObserver = window.ResizeObserver;\n",
+       "        } else {\n",
+       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+       "            this.ResizeObserver = obs.ResizeObserver;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+       "        var nentries = entries.length;\n",
+       "        for (var i = 0; i < nentries; i++) {\n",
+       "            var entry = entries[i];\n",
+       "            var width, height;\n",
+       "            if (entry.contentBoxSize) {\n",
+       "                if (entry.contentBoxSize instanceof Array) {\n",
+       "                    // Chrome 84 implements new version of spec.\n",
+       "                    width = entry.contentBoxSize[0].inlineSize;\n",
+       "                    height = entry.contentBoxSize[0].blockSize;\n",
+       "                } else {\n",
+       "                    // Firefox implements old version of spec.\n",
+       "                    width = entry.contentBoxSize.inlineSize;\n",
+       "                    height = entry.contentBoxSize.blockSize;\n",
+       "                }\n",
+       "            } else {\n",
+       "                // Chrome <84 implements even older version of spec.\n",
+       "                width = entry.contentRect.width;\n",
+       "                height = entry.contentRect.height;\n",
+       "            }\n",
+       "\n",
+       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
+       "            // the canvas container.\n",
+       "            if (entry.devicePixelContentBoxSize) {\n",
+       "                // Chrome 84 implements new version of spec.\n",
+       "                canvas.setAttribute(\n",
+       "                    'width',\n",
+       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
+       "                );\n",
+       "                canvas.setAttribute(\n",
+       "                    'height',\n",
+       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
+       "                );\n",
+       "            } else {\n",
+       "                canvas.setAttribute('width', width * fig.ratio);\n",
+       "                canvas.setAttribute('height', height * fig.ratio);\n",
+       "            }\n",
+       "            canvas.setAttribute(\n",
+       "                'style',\n",
+       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
+       "            );\n",
+       "\n",
+       "            rubberband_canvas.setAttribute('width', width);\n",
+       "            rubberband_canvas.setAttribute('height', height);\n",
+       "\n",
+       "            // And update the size in Python. We ignore the initial 0/0 size\n",
+       "            // that occurs as the element is placed into the DOM, which should\n",
+       "            // otherwise not happen due to the minimum size styling.\n",
+       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+       "                fig.request_resize(width, height);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    this.resizeObserverInstance.observe(canvas_div);\n",
+       "\n",
+       "    function on_mouse_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.mouse_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousedown',\n",
+       "        on_mouse_event_closure('button_press')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseup',\n",
+       "        on_mouse_event_closure('button_release')\n",
+       "    );\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousemove',\n",
+       "        on_mouse_event_closure('motion_notify')\n",
+       "    );\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseenter',\n",
+       "        on_mouse_event_closure('figure_enter')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseleave',\n",
+       "        on_mouse_event_closure('figure_leave')\n",
+       "    );\n",
+       "\n",
+       "    canvas_div.addEventListener('wheel', function (event) {\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        on_mouse_event_closure('scroll')(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.appendChild(canvas);\n",
+       "    canvas_div.appendChild(rubberband_canvas);\n",
+       "\n",
+       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+       "    this.rubberband_context.strokeStyle = '#000000';\n",
+       "\n",
+       "    this._resize_canvas = function (width, height, forward) {\n",
+       "        if (forward) {\n",
+       "            canvas_div.style.width = width + 'px';\n",
+       "            canvas_div.style.height = height + 'px';\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+       "        event.preventDefault();\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus() {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'mpl-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'mpl-button-group';\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'mpl-button-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
+       "        button.classList = 'mpl-widget';\n",
+       "        button.setAttribute('role', 'button');\n",
+       "        button.setAttribute('aria-disabled', 'false');\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "\n",
+       "        var icon_img = document.createElement('img');\n",
+       "        icon_img.src = '_images/' + image + '.png';\n",
+       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+       "        icon_img.alt = tooltip;\n",
+       "        button.appendChild(icon_img);\n",
+       "\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker = document.createElement('select');\n",
+       "    fmt_picker.classList = 'mpl-widget';\n",
+       "    toolbar.appendChild(fmt_picker);\n",
+       "    this.format_dropdown = fmt_picker;\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = document.createElement('option');\n",
+       "        option.selected = fmt === mpl.default_extension;\n",
+       "        option.innerHTML = fmt;\n",
+       "        fmt_picker.appendChild(option);\n",
+       "    }\n",
+       "\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function (type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function () {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+       "        fig.send_message('refresh', {});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+       "    var x0 = msg['x0'] / fig.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+       "    var x1 = msg['x1'] / fig.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0,\n",
+       "        0,\n",
+       "        fig.canvas.width / fig.ratio,\n",
+       "        fig.canvas.height / fig.ratio\n",
+       "    );\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch (cursor) {\n",
+       "        case 0:\n",
+       "            cursor = 'pointer';\n",
+       "            break;\n",
+       "        case 1:\n",
+       "            cursor = 'default';\n",
+       "            break;\n",
+       "        case 2:\n",
+       "            cursor = 'crosshair';\n",
+       "            break;\n",
+       "        case 3:\n",
+       "            cursor = 'move';\n",
+       "            break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+       "    for (var key in msg) {\n",
+       "        if (!(key in fig.buttons)) {\n",
+       "            continue;\n",
+       "        }\n",
+       "        fig.buttons[key].disabled = !msg[key];\n",
+       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+       "    if (msg['mode'] === 'PAN') {\n",
+       "        fig.buttons['Pan'].classList.add('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    } else if (msg['mode'] === 'ZOOM') {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.add('active');\n",
+       "    } else {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message('ack', {});\n",
+       "};\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = 'image/png';\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src\n",
+       "                );\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data\n",
+       "            );\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        } else if (\n",
+       "            typeof evt.data === 'string' &&\n",
+       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+       "        ) {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig['handle_' + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\n",
+       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
+       "                msg\n",
+       "            );\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\n",
+       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+       "                    e,\n",
+       "                    e.stack,\n",
+       "                    msg\n",
+       "                );\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function (e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e) {\n",
+       "        e = window.event;\n",
+       "    }\n",
+       "    if (e.target) {\n",
+       "        targ = e.target;\n",
+       "    } else if (e.srcElement) {\n",
+       "        targ = e.srcElement;\n",
+       "    }\n",
+       "    if (targ.nodeType === 3) {\n",
+       "        // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "    }\n",
+       "\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    var boundingRect = targ.getBoundingClientRect();\n",
+       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+       "\n",
+       "    return { x: x, y: y };\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys(original) {\n",
+       "    return Object.keys(original).reduce(function (obj, key) {\n",
+       "        if (typeof original[key] !== 'object') {\n",
+       "            obj[key] = original[key];\n",
+       "        }\n",
+       "        return obj;\n",
+       "    }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event);\n",
+       "\n",
+       "    if (name === 'button_press') {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * this.ratio;\n",
+       "    var y = canvas_pos.y * this.ratio;\n",
+       "\n",
+       "    this.send_message(name, {\n",
+       "        x: x,\n",
+       "        y: y,\n",
+       "        button: event.button,\n",
+       "        step: event.step,\n",
+       "        guiEvent: simpleKeys(event),\n",
+       "    });\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function (event, name) {\n",
+       "    // Prevent repeat events\n",
+       "    if (name === 'key_press') {\n",
+       "        if (event.which === this._key) {\n",
+       "            return;\n",
+       "        } else {\n",
+       "            this._key = event.which;\n",
+       "        }\n",
+       "    }\n",
+       "    if (name === 'key_release') {\n",
+       "        this._key = null;\n",
+       "    }\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which !== 17) {\n",
+       "        value += 'ctrl+';\n",
+       "    }\n",
+       "    if (event.altKey && event.which !== 18) {\n",
+       "        value += 'alt+';\n",
+       "    }\n",
+       "    if (event.shiftKey && event.which !== 16) {\n",
+       "        value += 'shift+';\n",
+       "    }\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+       "    if (name === 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message('toolbar_button', { name: name });\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "\n",
+       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+       "// prettier-ignore\n",
+       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";/* global mpl */\n",
+       "\n",
+       "var comm_websocket_adapter = function (comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function () {\n",
+       "        comm.close();\n",
+       "    };\n",
+       "    ws.send = function (m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function (msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data']);\n",
+       "    });\n",
+       "    return ws;\n",
+       "};\n",
+       "\n",
+       "mpl.mpl_figure_comm = function (comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = document.getElementById(id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm);\n",
+       "\n",
+       "    function ondownload(figure, _format) {\n",
+       "        window.open(figure.canvas.toDataURL());\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element;\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error('Failed to find cell for figure', id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.cell_info[0].output_area.element.on(\n",
+       "        'cleared',\n",
+       "        { fig: fig },\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+       "    var width = fig.canvas.width / fig.ratio;\n",
+       "    fig.cell_info[0].output_area.element.off(\n",
+       "        'cleared',\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable();\n",
+       "    fig.parent_element.innerHTML =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "    fig.close_ws(fig, msg);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width / this.ratio;\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message('ack', {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () {\n",
+       "        fig.push_to_output();\n",
+       "    }, 1000);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'btn-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'btn-group';\n",
+       "    var button;\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'btn-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        button = fig.buttons[name] = document.createElement('button');\n",
+       "        button.classList = 'btn btn-default';\n",
+       "        button.href = '#';\n",
+       "        button.title = name;\n",
+       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message pull-right';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = document.createElement('div');\n",
+       "    buttongrp.classList = 'btn-group inline pull-right';\n",
+       "    button = document.createElement('button');\n",
+       "    button.classList = 'btn btn-mini btn-primary';\n",
+       "    button.href = '#';\n",
+       "    button.title = 'Stop Interaction';\n",
+       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+       "    button.addEventListener('click', function (_evt) {\n",
+       "        fig.handle_close(fig, {});\n",
+       "    });\n",
+       "    button.addEventListener(\n",
+       "        'mouseover',\n",
+       "        on_mouseover_closure('Stop Interaction')\n",
+       "    );\n",
+       "    buttongrp.appendChild(button);\n",
+       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+       "    var fig = event.data.fig;\n",
+       "    if (event.target !== this) {\n",
+       "        // Ignore bubbled events from children.\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.close_ws(fig, {});\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (el) {\n",
+       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.setAttribute('tabindex', 0);\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    } else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager) {\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "    }\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which === 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "};\n",
+       "\n",
+       "mpl.find_output_cell = function (html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i = 0; i < ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code') {\n",
+       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] === html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel !== null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target(\n",
+       "        'matplotlib',\n",
+       "        mpl.mpl_figure_comm\n",
+       "    );\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-9.0, 7.5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca(projection='3d')\n",
+    "\n",
+    "# plot field lines\n",
+    "for lx,ly,lz in zip(linex,liney,linez):\n",
+    "    ax.plot(lx,ly,lz,'-',markersize=1)\n",
+    "\n",
+    "# plot map\n",
+    "ax.scatter(x1,y1,z1,s=2,alpha=0.3)\n",
+    "Bt = Ba1.reshape(xi.shape)*1e9 # contour and color scale in nT \n",
+    "c = ax.contourf(xi,yi,Bt,alpha=1,zdir='z',offset=z-max(radii)*2,cmap='jet',\n",
+    "                  levels=np.linspace(Bt.min(),Bt.max(),50,endpoint=True))\n",
+    "fig.colorbar(c)\n",
+    "\n",
+    "# auto-scaling for profile plot\n",
+    "ptpmax = np.max((Ba2.ptp(),Ba3.ptp())) # dynamic range\n",
+    "autoscaling = np.max(radii) / ptpmax\n",
+    "\n",
+    "# plot x-profile\n",
+    "ax.scatter(x2,y2,z2,s=2,c='black',alpha=0.3)\n",
+    "ax.plot(x2,Ba2*autoscaling,zs=ymax,c='black',zdir='y')\n",
+    "\n",
+    "# plot y-profile\n",
+    "ax.scatter(x3,y3,z3,s=2,c='black',alpha=0.3)\n",
+    "ax.plot(y3,Ba3*autoscaling,zs=xmin,c='black',zdir='x')\n",
+    "\n",
+    "ax.set_xlabel('X')\n",
+    "ax.set_ylabel('Y')\n",
+    "ax.set_zlabel('Z')\n",
+    "\n",
+    "ax.set_xlim(xmin, xmax)\n",
+    "ax.set_ylim(ymin, ymax)\n",
+    "ax.set_zlim(z-max(radii)*2, max(radii)*1.5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/mag/Mag_FitProfile.ipynb b/Notebooks/mag/Mag_FitProfile.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..319e2064a26d2697d9701e838d1933a34566a210
--- /dev/null
+++ b/Notebooks/mag/Mag_FitProfile.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 10,
+        "hidden": false,
+        "row": 0,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) \n",
+    "\n",
+    "This notebook uses code adapted from \n",
+    "\n",
+    "SimPEG\n",
+    "- Cockett, R., S. Kang, L.J. Heagy, A. Pidlisecky, D.W. Oldenburg (2015, in review), SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers and Geosciences\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 10,
+        "width": 6
+       },
+       "report_default": {}
+      }
+     }
+    }
+   },
+   "source": [
+    "## View the model\n",
+    "\n",
+    "- dx: width or prism in x-direction (m)\n",
+    "- dy: width of prism in y-direction (m)\n",
+    "- dz: vertical extent of prism (m)\n",
+    "- x0: x location of the center of the prism (m)\n",
+    "- y0: y location of the center of the prism (m)\n",
+    "- depth: depth to the top of the prism (m)\n",
+    "- prism_inc: inclination of the prism (reference is a unit northing vector; degrees)\n",
+    "- prism_dec: declination of the prism (reference is a unit northing vector; degrees)\n",
+    "- View_dip: dip angle of view (degrees)\n",
+    "- View_elev: elevation of view (degrees)\n",
+    "- View_azim: azimuth of view (degrees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 29,
+        "hidden": false,
+        "row": 21,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from geoscilabs.mag import Mag, Simulator\n",
+    "from discretize import TensorMesh\n",
+    "from SimPEG import utils\n",
+    "from SimPEG.potential_fields import magnetics as mag\n",
+    "from ipywidgets import widgets\n",
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from SimPEG.utils import download\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77bbf8705d2a4626bbe0658fcd3a6416",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='Refresh'), FloatSlider(value=2.375, continuous_up…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Input parameters\n",
+    "\n",
+    "#fileName = 'http://github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/Lab1_Wednesday_TA.csv'\n",
+    "#synData  = download(fileName,overwrite=True,verbose=False)\n",
+    "\n",
+    "fileName = os.getcwd()+ \"/Lab1_Wednesday_TA.csv\"\n",
+    "\n",
+    "data = np.genfromtxt(fileName, skip_header=1, delimiter=',')\n",
+    "xyzd = np.c_[np.zeros(data.shape[0]), data[:,0], np.zeros(data.shape[0]), data[:,1]]\n",
+    "B = np.r_[60308, 83.8, 25.4]\n",
+    "survey, dobj = Mag.createMagSurvey(xyzd, B)\n",
+    "# View the data and chose a profile\n",
+    "# Define the parametric model interactively\n",
+    "model = Simulator.ViewPrism(survey)\n",
+    "display(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit the data\n",
+    "- Binc: Inclination of the Earth's background field (degree)\n",
+    "- Bdec: Declination of the Earth's background field (degree)\n",
+    "- Bigrf: Strength of the Earth's background field (nT) \n",
+    "- depth: vertical distance from the sensor to the top of the rebar (m)\n",
+    "- susc: magnetic susceptibility\n",
+    "- comp: Total field (tf) of component of the field to plot\n",
+    "- irt: Type of magnetization \n",
+    "- Q: Koenigsberger ratio ($\\frac{M_{rem}}{M_{ind}}$)\n",
+    "- rinc: inclination of the remanent magnetization (degree)\n",
+    "- rdec: declination of the remanent magnetization (degree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 6,
+        "height": 29,
+        "hidden": false,
+        "row": 21,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "012534ac73914e05a943624e10942b80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=90.0, continuous_update=False, description='Binc', max=90.0, min=-90.0…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q = Simulator.fitline(model,survey,dobj)\n",
+    "display(Q)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "grid_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "8789ea09a28443d0b196412593e51195": {
+     "views": [
+      {
+       "cell_index": 3
+      }
+     ]
+    },
+    "8bd53575b8d6470cb6158b113159182e": {
+     "views": [
+      {
+       "cell_index": 5
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/mag/Mag_Induced2D.ipynb b/Notebooks/mag/Mag_Induced2D.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a348ba91f2463c06f0869e30a9db0efd2bf58344
--- /dev/null
+++ b/Notebooks/mag/Mag_Induced2D.ipynb
@@ -0,0 +1,419 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 0,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) \n",
+    "\n",
+    "This notebook uses code adapted from \n",
+    "\n",
+    "SimPEG\n",
+    "- Cockett, R., S. Kang, L.J. Heagy, A. Pidlisecky, D.W. Oldenburg (2015, in review), SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers and Geosciences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 3,
+        "hidden": true,
+        "row": 11,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from geoscilabs.mag import Mag, Simulator\n",
+    "from SimPEG.potential_fields import magnetics as mag\n",
+    "from SimPEG import utils, data\n",
+    "from discretize import TensorMesh\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 21,
+        "hidden": false,
+        "row": 22,
+        "width": null
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# How do we define direction of an earth magnetic field?\n",
+    "\n",
+    "Earth magnetic field is a vector. To define a vector we need to choose a coordinate system. We use right-handed system: \n",
+    "- X (Easting), \n",
+    "- Y (Northing), and \n",
+    "- Z (Up). \n",
+    "\n",
+    "Here we consider an earth magnetic field ($\\vec{B_0}$), of which intensity is one. To define this unit vector, we use inclinatino and declination:\n",
+    "- Declination: An angle from geographic North (Ng) (positive clockwise)\n",
+    "- Inclination: Vertical angle from the N-E plane (positive down)\n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/raw/main/images/mag/earthfield.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 18,
+        "hidden": false,
+        "row": 43,
+        "width": null
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# What's data: total field anomaly\n",
+    "\n",
+    "We consider a typical form of magnetic data. To illustrate this we consider an suceptible object embedded in the earth. \n",
+    "Based upon the earth magnetic field ($\\vec{B}_0$), this object will generate anomalous magnetic field ($\\vec{B}_A$). We define an unit vector $\\hat{B}_0$ for the earth field as \n",
+    "$$ \\hat{B}_0 = \\frac{\\vec{B}_0}{|\\vec{B}_0|}$$ \n",
+    "We measure both earth and anomalous magnetic field such that\n",
+    "\n",
+    "$$ \\vec{B} = \\vec{B}_0 + \\vec{B}_A$$\n",
+    "\n",
+    "Total field anomaly, $\\triangle \\vec{B}$ can be defined as\n",
+    "\n",
+    "$$  |\\triangle \\vec{B}| = |\\vec{B}|-|\\vec{B}_E| $$ \n",
+    "\n",
+    "If $|\\vec{B}|\\ll|\\vec{B}_E|$, then that is total field anomaly $\\triangle \\vec{B}$ is the projection of the anomalous field onto the direction of the earth field:\n",
+    "\n",
+    "$$ |\\triangle \\vec{B}| \\simeq \\vec{B}_A \\cdot \\hat{B}_0=|\\vec{B}_A|cos\\theta$$ \n",
+    "\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/raw/main/images/mag/totalfieldanomaly.png?raw=true\" style=\"width: 50%; height: 50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 11,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# Define a 3D prism\n",
+    "Our model is a rectangular prism. Parameters to define this prism are given below:\n",
+    "\n",
+    "- dx: length in Easting (x) direction (meter)\n",
+    "- dy: length in Northing (y) direction (meter)\n",
+    "- dz: length in Depth (z) direction (meter) below the receiver\n",
+    "- depth: top boundary of the prism (meter)\n",
+    "- pinc: inclination of the prism (reference is a unit northing vector; degree)\n",
+    "- pdec: declination of the prism (reference is a unit northing vector; degree)\n",
+    "\n",
+    "You can also change the height of the survey grid above the ground\n",
+    "- rx_h: height of the grid (meter)\n",
+    "\n",
+    "*Green dots show a plane where we measure data.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 28,
+        "hidden": false,
+        "row": 61,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82d6267941c242a3acd81042ed90a234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=557366.1875, continuous_update=False, description='East', max=558589.8…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Input parameters\n",
+    "\n",
+    "#fileName = 'https://github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/DO27_TMI.dat'\n",
+    "\n",
+    "fileName = os.getcwd()+ \"/DO27_TMI.dat\"\n",
+    "\n",
+    "\n",
+    "xyzd = np.genfromtxt(fileName, skip_header=3)\n",
+    "B = np.r_[60308, 83.8, 25.4]\n",
+    "survey, dobj = Mag.createMagSurvey(xyzd, B)\n",
+    "# View the data and chose a profile\n",
+    "param = Simulator.ViewMagSurvey2D(survey, dobj)\n",
+    "display(param)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a98e5f0cd294c96a2f78c89ea44ac08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='Refresh'), FloatSlider(value=152.953125, continuo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the parametric model interactively\n",
+    "model = Simulator.ViewPrism(param.result)\n",
+    "display(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 6,
+        "height": 11,
+        "hidden": false,
+        "row": 11,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "# Magnetic applet\n",
+    "Based on the prism that you made above, below Magnetic applet computes magnetic field at receiver locations, and provide both 2D map (left) and profile line (right). \n",
+    "\n",
+    "For the prism, you can alter:\n",
+    "- sus: susceptibility of the prism\n",
+    "\n",
+    "Parameters for the earth field are:\n",
+    "- Einc: inclination of the earth field (degree)\n",
+    "- Edec: declination of the earth field (degree)\n",
+    "- Bigrf: intensity of the earth field (nT)\n",
+    "\n",
+    "For data, you can view:\n",
+    "- tf: total field anomaly,  \n",
+    "- bx :x-component, \n",
+    "- by :y-component, \n",
+    "- bz :z-component\n",
+    "\n",
+    "You can simulate and view remanent magnetization effect with parameters:\n",
+    "- irt: \"induced\", \"remanent\", or \"total\"\n",
+    "- Q: Koenigsberger ratio ($\\frac{M_{rem}}{M_{ind}}$)\n",
+    "- rinc: inclination of the remanent magnetization (degree)\n",
+    "- rdec: declination of the remanent magnetization (degree)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 6,
+        "height": 28,
+        "hidden": false,
+        "row": 61,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c43af169fe743a9832809433addb4c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(ToggleButton(value=False, description='Refresh'), FloatSlider(value=0.1, continuous_upda…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotwidget = Simulator.PFSimulator(model, param)\n",
+    "display(plotwidget)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "report_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "2c77e5c891dd44069234331d87475ef2": {
+     "views": [
+      {
+       "cell_index": 5
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/mag/MagneticDipoleApplet.ipynb b/Notebooks/mag/MagneticDipoleApplet.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b47fe04580c156ffdb3c2a7538ab7dc478c4f4cf
--- /dev/null
+++ b/Notebooks/mag/MagneticDipoleApplet.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.mag.MagDipoleApp import MagneticDipoleApp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Magnetic Dipole Applet\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a small buried dipolar magnet. In geophysics, this simulates the observed anomaly over a buried susceptible sphere that is magnetized by the Earth's magnetic field.\n",
+    "\n",
+    "## What is shown\n",
+    "\n",
+    "- <b>The colour map</b> shows the strength of the chosen parameter (Bt, Bx, By, Bz, or Bg) as a function of position.\n",
+    "\n",
+    "- Imagine doing a two dimensional survey over a susceptible sphere that has been magentized by the Earth's magnetic field specified by inclination and declination.  \"Measurement\" location is the centre of each coloured box. This is a simple (but easily programmable) alternative to generating a smooth contour map.\n",
+    "\n",
+    "- The anomaly depends upon magnetic latitude, direction of the inducing (Earth's) field, the depth of the buried dipole, and the magnetic moment of the buried dipole.\n",
+    "\n",
+    "\n",
+    "## Important Notes:\n",
+    "\n",
+    "- <b>Inclination (I)</b> and <b>declination (D)</b> describe the orientation of the Earth's ambient field at the centre of the survey area. Positive inclination implies you are in the northern hemisphere, and positive declination implies that magnetic north is to the east of geographic north.\n",
+    "\n",
+    "- The <b>\"length\"</b> adjuster changes the size of the square survey area. The default of 72 means the survey square is 72 metres on a side.\n",
+    "\n",
+    "- The <b>\"data spacing\"</b> adjuster changes the distance between measurements. The default of 1 means measurements were acquired over the survey square on a 2-metre grid. In other words, \"data spacing = 2\" means each coloured box is 2 m square.\n",
+    "\n",
+    "- The <b>\"depth\"</b> adjuster changes the depth (in metres) to the centre of the buried dipole.\n",
+    "\n",
+    "- The <b>\"magnetic moment (M)\"</b> adjuster changes the strength of the induced field. Units are Am2.  This is related to the strength of the inducing field, the susceptibility of the buried sphere, and the volume of susceptible material.\n",
+    "- <b>Bt, Bg, Bx, By, Bz</b> are Total field, X-component (positive northwards), Y-component (positive eastwards), and Z-component (positive down) of the anomaly field respectively.\n",
+    "\n",
+    "- Checking the <b>fixed scale</b> button fixes the colour scale so that the end points of the colour scale are minimum and maximum values for the current data set.\n",
+    "\n",
+    "- You can generate a <b>profile</b> along either \"East\" or \"North\" direction\n",
+    "\n",
+    "- Check <b>half width</b> to see the half width of the anomaly. Anomaly width is noted on the botton of the graph.\n",
+    "\n",
+    "- Measurements are taken 1m above the surface.\n",
+    "\n",
+    "- For gradient data (<b>Bg</b>), measurements are taken at 1m and 2m\n",
+    "\n",
+    "- Note that magnetic moment (M) for monopole is equal to the charge (Q): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8add133a23c4749a81f269ec705d44f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(RadioButtons(description='field', options=('Bt', 'Bx', 'By', 'Bz', 'Bg'), value=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag = MagneticDipoleApp()\n",
+    "mag.interact_plot_model_dipole()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two monopoles (pseudo dipole)\n",
+    "\n",
+    "Different from the previous app, here we focus on a situtation where we have to monopoles having negative\n",
+    "and postive signs. Their horizontal location: (X, Y) are same, but depths are different. By default depth of the negative pole, <b>depth$_{-Q}$ </b>, is located 0m and that of the positive pole, <b>depth$_{-Q}$ </b>, is 10m. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e22ddaaa4fb439eb9e967b48d81568f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(RadioButtons(description='field', options=('Bt', 'Bx', 'By', 'Bz', 'Bg'), value=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag.interact_plot_model_two_monopole()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
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+   "version": "3.7.10"
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+}
diff --git a/Notebooks/mag/MagneticPrismApplet.ipynb b/Notebooks/mag/MagneticPrismApplet.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cf568dcd894c70fe9cca2f3da1e347f63be168d7
--- /dev/null
+++ b/Notebooks/mag/MagneticPrismApplet.ipynb
@@ -0,0 +1,230 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 0,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "This is the <a href=\"https://jupyter.org/\">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it. \n",
+    "\n",
+    "To use the notebook:\n",
+    "- \"Shift + Enter\" runs the code within the cell (so does the forward arrow button near the top of the document)\n",
+    "- You can alter variables and re-run cells\n",
+    "- If you want to start with a clean slate, restart the Kernel either by going to the top, clicking on Kernel: Restart, or by \"esc + 00\" (if you do this, you will need to re-run the following block of code before running any other cells in the notebook) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geoscilabs.mag.MagDipoleApp import MagneticDipoleApp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Magnetic Prism Applet\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 3,
+        "hidden": true,
+        "row": 11,
+        "width": 12
+       },
+       "report_default": {
+        "hidden": true
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "From the Magnetic Dipole applet, we have learned how anomalous magnetic field observed at ground's surface look\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a retangular susceptible prism. \n",
+    "\n",
+    "\n",
+    "## What is shown\n",
+    "\n",
+    "- <b>The colour map</b> shows the strength of the chosen parameter (Bt, Bx, By, or Bz) as a function of position.\n",
+    "\n",
+    "- Imagine doing a two dimensional survey over a susceptible sphere that has been magentized by the Earth's magnetic field specified by inclination and declination.  \"Measurement\" location is the centre of each coloured box. This is a simple (but easily programmable) alternative to generating a smooth contour map.\n",
+    "\n",
+    "- The anomaly depends upon magnetic latitude, direction of the inducing (Earth's) field, the depth of the buried dipole, and the magnetic moment of the buried dipole.\n",
+    "\n",
+    "\n",
+    "## Important Notes:\n",
+    "\n",
+    "- <b>Inclination (I)</b> and <b>declination (D)</b> describe the orientation of the Earth's ambient field at the centre of the survey area. Positive inclination implies you are in the northern hemisphere, and positive declination implies that magnetic north is to the east of geographic north.\n",
+    "\n",
+    "- The <b>\"length\"</b> adjuster changes the size of the square survey area. The default of 72 means the survey square is 72 metres on a side.\n",
+    "\n",
+    "- The <b>\"data spacing\"</b> adjuster changes the distance between measurements. The default of 1 means measurements were acquired over the survey square on a 2-metre grid. In other words, \"data spacing = 2\" means each coloured box is 2 m square.\n",
+    "\n",
+    "- The <b>\"depth\"</b> adjuster changes the depth (in metres) to the top of the buried prism.\n",
+    "\n",
+    "- The <b>\"magnetic moment (M)\"</b> adjuster changes the strength of the induced field. Units are Am2.  This is related to the strength of the inducing field, the susceptibility of the buried sphere, and the volume of susceptible material.\n",
+    "- <b>Bt, Bx, By, Bz</b> are Total field, X-component (positive northwards), Y-component (positive eastwards), and Z-component (positive down) of the anomaly field respectively.\n",
+    "\n",
+    "- Checking the <b>fixed scale</b> button fixes the colour scale so that the end points of the colour scale are minimum and maximum values for the current data set.\n",
+    "\n",
+    "- You can generate a <b>profile</b> along either \"East\" or \"North\" direction\n",
+    "\n",
+    "- Check <b>half width</b> to see the half width of the anomaly. Anomaly width is noted on the botton of the graph.\n",
+    "\n",
+    "- Measurements are taken 1m above the surface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "extensions": {
+     "jupyter_dashboards": {
+      "version": 1,
+      "views": {
+       "grid_default": {
+        "col": 0,
+        "height": 11,
+        "hidden": false,
+        "row": 11,
+        "width": 6
+       },
+       "report_default": {
+        "hidden": false
+       }
+      }
+     }
+    }
+   },
+   "source": [
+    "## Define a 3D prism\n",
+    "Compared to the MagneticDipoleApplet, there are additional parameters to define a prism.\n",
+    "\n",
+    "- $\\triangle x$: length in North (X) direction (m)\n",
+    "- $\\triangle y$: length in East (Y) direction (m)\n",
+    "- $\\triangle z$: length in Depth (z) direction (m) below the receiver\n",
+    "- depth: top boundary of the prism (meter)\n",
+    "- I$_{prism}$: inclination of the prism (reference is north direction)\n",
+    "- D$_{prism}$: declination of the prism (reference is north direction) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64d0ad8c47ad4681b4bbb15180cb6042",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(RadioButtons(description='plot', options=('field', 'model'), value='field'), Rad…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mag = MagneticDipoleApp()\n",
+    "mag.interact_plot_model_prism()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "extensions": {
+   "jupyter_dashboards": {
+    "activeView": "report_default",
+    "version": 1,
+    "views": {
+     "grid_default": {
+      "cellMargin": 10,
+      "defaultCellHeight": 20,
+      "maxColumns": 12,
+      "name": "grid",
+      "type": "grid"
+     },
+     "report_default": {
+      "name": "report",
+      "type": "report"
+     }
+    }
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "widgets": {
+   "state": {
+    "2c77e5c891dd44069234331d87475ef2": {
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+      {
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+     ]
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Notebooks/mag/github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/DO27_TMI.dat b/Notebooks/mag/github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/DO27_TMI.dat
new file mode 100644
index 0000000000000000000000000000000000000000..ea4d95c3a7ac408f6e077563d44b3b817b426ac5
--- /dev/null
+++ b/Notebooks/mag/github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/DO27_TMI.dat
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diff --git a/Notebooks/mag/github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/Lab1_Wednesday_TA.csv b/Notebooks/mag/github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/Lab1_Wednesday_TA.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ebc9df34171d3a6ade4ad25cecc6cb05b15a946a
--- /dev/null
+++ b/Notebooks/mag/github.com/geoscixyz/geosci-labs/raw/main/assets/mag/data/Lab1_Wednesday_TA.csv
@@ -0,0 +1,24 @@
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+1,54401,1.15
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+12,54487.83333,19.32
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+13,54789.25,48.3
+13,54070.44444,20.55
+13.5,54042.88889,57.83
+14,54031.33333,9.87
+14.5,54017.77778,4.03
+15,54000.22222,20.7
+16,54369.66667,2.22
+18,54384.11111,1.15
+20,54390.22222,2.08
diff --git a/Notebooks/seismic/.ipynb_checkpoints/2D-LinearInversion-Crosswell-Tomorgraphy-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/2D-LinearInversion-Crosswell-Tomorgraphy-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..11ce189a952e70ffcb70ba7d80314a53a07cd5b1
--- /dev/null
+++ b/Notebooks/seismic/.ipynb_checkpoints/2D-LinearInversion-Crosswell-Tomorgraphy-checkpoint.ipynb
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2D Linear Inversion of Crosswell Tomography Data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from geoscilabs.inversion.TomographyInversion import TomographyInversionApp\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import interact, IntSlider\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Purpose\n",
+    "\n",
+    "From the Linear inversion notebook (1D), we learned important apsects about the linear inversion. \n",
+    "However, real world geophysical inverse problem are not often 1D, but multidimensional (2D or 3D), and this extension of dimension allows us to put more apriori (or geologic) information through the regularization term. \n",
+    "In this notebook, we explore these multidimensional aspects of the linear inversion by using 2D traveltime croswell tomography example. \n",
+    "\n",
+    "## Outline\n",
+    "This notebook includes four steps:\n",
+    "- Step1: Generate a velocity model\n",
+    "- Step2: Simulate traveltime data and add noise\n",
+    "- Step3: Run $l_2$ inversion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step1: Generate a velocity model\n",
+    "\n",
+    "Here we set up a velocity model using a following app. Controlling parameters of the app are:\n",
+    "\n",
+    "- `set mesh`: use active **only** when you want to change the 2D mesh\n",
+    "- `add block`: use active when you want to add block (if not stay inactive)\n",
+    "- `model type`: background or block\n",
+    "- `show grid?`: show grid of the mesh\n",
+    "\n",
+    "- `v0`: velocity of the background\n",
+    "- `v1`: velocity of the block\n",
+    "- `xc`: x center of the block\n",
+    "- `yc`: y center of the block\n",
+    "- `dx`: width of the block\n",
+    "- `dy`: thickness of the block\n",
+    "- `nx`: # of cells in x-direction (this is only active when `set_mesh=active`)\n",
+    "- `ny`: # of cells in y-direction (this is only active when `set_mesh=active`)\n",
+    "\n",
+    "### Changing # of cells in $x$- or $z$- direction\n",
+    "Related parameters for this task are: `set mesh`, `nx`, `ny`. \n",
+    "Size of the 2D domain are fixed to 200m $\\times$ 400m, but the number of cells in each direction can be changed such that you can alter size of the cells in each direction. When you change either `nx` or `ny` make sure you choose `set mesh=active` otherwise `set mesh=inactive`. Note that once mesh setup is changed, velocity model is reset to a background value (`v0`). \n",
+    "\n",
+    "### Changing a parameter of a single block\n",
+    "Although you can change the location, size, and velocity of the block there are few rules that you need to follow to do so. \n",
+    "\n",
+    "1. If you want to change the parameter of the block: first set `add block=active` then change parameters of the block (`v1`, `xc`, `zc`, `dx`, `dy`)\n",
+    "\n",
+    "2. Once you changed the parameters, make sure first choose `model type=background` then change that to `model type=block`\n",
+    "\n",
+    "### Adding more blocks\n",
+    "You can also add multiple blocks. To add a block follow below steps:\n",
+    "\n",
+    "1. Set `add block=inactive`, then change the parameter of the new block using: `v1`, `xc`, `zc`, `dx`, `dy`. Velocity model will not change, but you can see the white lines which illustrate boundary of the new block.\n",
+    "\n",
+    "2. Once you are happy with the new block, set `add block=active`, then velocity model will be updated with the new block that you have set. \n",
+    "\n",
+    "3. Repeat 1 and 2 if you want to add more blocks. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58a1dbadf6f9499ca06c052f2653299a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(FloatSlider(value=1000.0, continuous_update=False, description='v0', max=3000.0,…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app = TomographyInversionApp()\n",
+    "app.interact_plot_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step2: Simulate travel time data and add noise\n",
+    "\n",
+    "Within this app, by using the velocity model set up, we simulate traveltime tomography data and add Gaussian noise. This syntehtic data set will be used in the following inversion module. Controlling parameters are:\n",
+    "\n",
+    "- `percent (%)`: percentage of the Gaussian noise\n",
+    "- `floor (s)`: floor of the Gaussain noise\n",
+    "- `random seed`: seed to generate random variables having normal distribution\n",
+    "- `tx_rx_plane` or `histogram`: choice of the plotting data\n",
+    "- `update`: this buttion is for the interactin between the first app. If the velocity model is changed by altering the first app, you can simply click `update` to run simulation again with the updated velocity model. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6dd36a9bb6a435cb5f4aff7cdb1a47a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(), Box(children=(Box(children=(BoundedFloatText(value=0.0, description='percent ($\\\\%$):…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app.interact_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step3: $l_2$ inversion\n",
+    "\n",
+    "- `maxIter`: maximum number of iteration\n",
+    "- `m0`: initial model\n",
+    "- `mref`: reference model\n",
+    "- `percentage`: percent standard deviation for the uncertainty\n",
+    "- `floor`: floor value for the uncertainty\n",
+    "- `chifact`: chifactor for the target misfit\n",
+    "- `beta0_ratio`: ratio to set the initial beta\n",
+    "- `coolingFactor`: cooling factor to cool beta\n",
+    "- `n_iter_per_beta`: # of interation for each beta value \n",
+    "- `alpha_s`: $\\alpha_s$\n",
+    "- `alpha_x`: $\\alpha_x$\n",
+    "- `alpha_z`: $\\alpha_z$\n",
+    "- `use_target`: use target misfit as a stopping criteria or not"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-1: Run inversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "\n",
+      "        SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+      "        ***Done using same Solver and solverOpts as the problem***\n",
+      "SimPEG.SaveOutputEveryIteration will save your inversion progress as: '###-InversionModel-2021-06-17-14-51.txt'\n",
+      "model has any nan: 0\n",
+      "=============================== Projected GNCG ===============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "x0 has any nan: 0\n",
+      "   0  2.39e+06  3.40e+02  4.00e-02  9.60e+04    1.41e-02      0              \n",
+      "   1  1.20e+06  6.76e+02  3.53e-06  6.81e+02    3.20e+05      0              \n",
+      "   2  5.98e+05  6.60e+02  1.37e-05  6.68e+02    3.15e+05      0              \n",
+      "   3  2.99e+05  6.30e+02  5.14e-05  6.45e+02    3.05e+05      0   Skip BFGS  \n",
+      "   4  1.50e+05  5.77e+02  1.83e-04  6.04e+02    2.87e+05      0   Skip BFGS  \n",
+      "   5  7.48e+04  4.94e+02  5.91e-04  5.38e+02    2.59e+05      0   Skip BFGS  \n",
+      "   6  3.74e+04  3.84e+02  1.66e-03  4.46e+02    2.17e+05      0   Skip BFGS  \n",
+      "   7  1.87e+04  2.66e+02  3.93e-03  3.40e+02    1.67e+05      0   Skip BFGS  \n",
+      "   8  9.35e+03  1.65e+02  7.79e-03  2.38e+02    1.18e+05      0   Skip BFGS  \n",
+      "   9  4.67e+03  9.27e+01  1.32e-02  1.54e+02    7.65e+04      0   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 9.6048e+03\n",
+      "1 : |xc-x_last| = 4.0147e-04 <= tolX*(1+|x0|) = 1.0141e-01\n",
+      "0 : |proj(x-g)-x|    = 7.6521e+04 <= tolG          = 1.0000e-10\n",
+      "0 : |proj(x-g)-x|    = 7.6521e+04 <= 1e3*eps       = 1.0000e-07\n",
+      "0 : maxIter   =      20    <= iter          =     10\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model, pred, save = app.run_inversion(\n",
+    "    maxIter=20,\n",
+    "    m0=1./1000.,\n",
+    "    mref=1./1250.,\n",
+    "    percentage=0,\n",
+    "    floor=0.01,\n",
+    "    chifact=1,\n",
+    "    beta0_ratio=1e2,\n",
+    "    coolingFactor=2,\n",
+    "    n_iter_per_beta=1,\n",
+    "    alpha_s=1/(app.mesh_prop.hx.min())**2 * 1e4,\n",
+    "    alpha_x=1,\n",
+    "    alpha_z=1,\n",
+    "    use_target=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-2: Plot recovered model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "118c205b4a3a4a32980d288bce96a376",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, description='ii', max=10), Checkbox(value=False, description='fixed'…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app.interact_model_inversion(model, clim=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-3: Plot predicted data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c17a3df42624acbb4f036b7d4445e0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, continuous_update=False, description='ii', max=10), Checkbox(value=F…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app.interact_data_inversion(pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-4: Plot misfit curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "save.plot_misfit_curves()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "save.plot_tikhonov_curves()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/seismic/.ipynb_checkpoints/LinearInversion-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/LinearInversion-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c45f5adc9663a0f0def415f95ef0780115e6e849
--- /dev/null
+++ b/Notebooks/seismic/.ipynb_checkpoints/LinearInversion-checkpoint.ipynb
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "8hoXeANtrV3ZUHZuBUa8",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     },
+     "outputId": {
+      "block": "c3ct5bmxGb0kJAJA80y1",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from geoscilabs.inversion.LinearInversionDirect import LinearInversionDirectApp\n",
+    "from ipywidgets import interact, FloatSlider, ToggleButtons, IntSlider, FloatText, IntText"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "XoBdBeGBCevcAMVqqZtM",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     },
+     "outputId": {
+      "block": "nzHSDMZI4foQGe88I1D5",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "app = LinearInversionDirectApp()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "GAKaomYwcnkDpl2fpbAI",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "# Linear Inversion App\n",
+    "\n",
+    "This app is based upon the inversion tutorial: \"INVERSION FOR APPLIED GEOPHYSICS\" by Oldenburg and Li (2005).\n",
+    "\n",
+    "Douglas W. Oldenburg and Yaoguo Li (2005) 5. Inversion for Applied Geophysics: A Tutorial. Near-Surface Geophysics: pp. 89-150.\n",
+    "eISBN: 978-1-56080-171-9 \n",
+    "print ISBN: 978-1-56080-130-6 \n",
+    "https://doi.org/10.1190/1.9781560801719.ch5 \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "wzGxtmJjFucP0mYtZzVD",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 2
+     }
+    }
+   },
+   "source": [
+    "## Purpose\n",
+    "\n",
+    "By using a simple decaying and oscillating kernel function, which emulates the physics of electromagnetic (EM) survey, we understand basic concepts of inverting data. Three items that we are going to explore are:\n",
+    "\n",
+    "- Step1: Create a model ($\\mathbf{m}$)\n",
+    "- Step2: Generate a sensitivity kernel (or matrix), $\\mathbf{G}$\n",
+    "- Step3: Simulate data ($\\mathbf{d} = \\mathbf{G}\\mathbf{m}$)\n",
+    "- Step4: All three steps together\n",
+    "- Step5: Invert the data, and explore inversion results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "TVYiH721oymtWnC2zQ1w",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 2
+     }
+    }
+   },
+   "source": [
+    "## Forward problem\n",
+    "\n",
+    "\n",
+    "Let $g_j(x)$ denote the kernel function for $j$th datum. With a given model $m(x)$, $j$th datum can be computed by solving following integral equation:\n",
+    "\n",
+    " $$ d_j = \\int_a^{b} g_j(x) m(x) dx $$\n",
+    "\n",
+    "where \n",
+    "\n",
+    "$$ g_j(x) = e^{p_jx} cos (2 \\pi q_jx) $$ \n",
+    "\n",
+    "By discretizing $g_j(x)$ we obtain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "s9H8M9Mtzk7wkXBPF8iZ",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 2
+     }
+    }
+   },
+   "source": [
+    "$$ \\mathbf{g}_j(\\mathbf{x}) = e^{p_j\\mathbf{x}} cos (2 \\pi q_j \\mathbf{x})$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "- $\\mathbf{g}_j$: $j$th row vector for the sensitivty matrix ($1 \\times M$)\n",
+    "- $\\mathbf{x}$: model location ($1 \\times M$)\n",
+    "- $p_j$: decaying constant (<0)\n",
+    "- $q_j$: oscillating constant (>0)\n",
+    "\n",
+    "By stacking multiple rows of $\\mathbf{g}_j$, we obtain sensitivity matrix, $\\mathbf{G}$: \n",
+    "\n",
+    "\\begin{align}\n",
+    "    \\mathbf{G} = \n",
+    "    \\begin{bmatrix}\n",
+    "        \\mathbf{g}_1\\\\\n",
+    "        \\vdots\\\\\n",
+    "        \\mathbf{g}_{N}\n",
+    "    \\end{bmatrix}\n",
+    "\\end{align}\n",
+    "\n",
+    "Here, the size of the matrix $\\mathbf{G}$ is $(N \\times M)$. \n",
+    "Finally data, $\\mathbf{d}$, can be written as a linear equation:\n",
+    "\n",
+    "$$ \\mathbf{d} = \\mathbf{G}\\mathbf{m}$$\n",
+    "\n",
+    "where $\\mathbf{m}$ is an inversion model; this is a column vector ($M \\times 1$). \n",
+    "\n",
+    "In real measurments, there will be various noises source, and hence observation, $\\mathbf{d}^{obs}$, can be written as \n",
+    "\n",
+    "$$ \\mathbf{d}^{obs} = \\mathbf{G}\\mathbf{m} + \\mathbf{noise}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "UY71pbGgRaPr7PBxH3OT",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "##  Step1: Create a model, $\\mathbf{m}$\n",
+    "\n",
+    "The model $m$ is a function defined on the interval (-2,2). Here we generate a model that is the sum of a: (a) background $m_{ref}$, (b) box car $m_1$ and (c) Gaussian $m_2$. The box car is defined by\n",
+    "- `m$_{background}$` : amplitude of the background\n",
+    "- `m1` : amplitude\n",
+    "- `$m1_{center}$` : center\n",
+    "- `$m1_{width}$` : width\n",
+    "the Gaussian is defined by \n",
+    "- `m2` : amplitude\n",
+    "- `$m2_{center}$` : center\n",
+    "- `$m2_{sigma}$` : width of Gaussian (as defined by a standard deviation)\n",
+    "- `M`: # of model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80ec4d5942d74227b59639192f817751",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='m$_{background}$', max=2.0,…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q_model = app.interact_plot_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "0zUv3gNqulptgg5RQABq",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "##  Step2: Generate a sensitivity kernel (or matrix), $\\mathbf{G}$\n",
+    "\n",
+    "By using the following app, we explore each row vector of the sensitivity matrix, $\\mathbf{g}_j$. Parameters of the apps are:\n",
+    "\n",
+    "- `M`: # of model parameters\n",
+    "- `N`: # of data\n",
+    "- `p`: decaying constant (<0)\n",
+    "- `q`: oscillating constant (>0)\n",
+    "- `ymin`: maximum limit for y-axis\n",
+    "- `ymax`: minimum limit for y-axis\n",
+    "- `show_singular`: show singualr values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "x9PxRUeZbvYukM3fIzi0",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 3
+     },
+     "outputId": {
+      "block": "xjP1IRGiznblm7UECCWU",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 3
+     }
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fee53dd89d6e44bbb00f4bb390a2ffaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=20, continuous_update=False, description='N', min=1), IntSlider(value=10…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q_kernel = app.interact_plot_G()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "B3aFbgUWKTzmqttCPDu3",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "## Step3: Simulate data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "fOWoINnfZAyspfxAKRnr",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "The $j$-th datum is the inner product of the $j$-th kernel $g_j(x)$ and the model $m(x)$. In discrete form it can be written as the dot product of the vector $g_j$ and the model vector $m$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "xeKrzyj4YLEYLfraRG1y",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "### $$ d_j = \\mathbf{g}_j \\mathbf{m} $$\n",
+    "\n",
+    "If there are $N$ data, these data can be written as a column vector, $\\mathbf{d}$:\n",
+    "\n",
+    "\\begin{align}\n",
+    "    \\mathbf{d} = \\mathbf{G}\\mathbf{m} = \n",
+    "    \\begin{bmatrix}\n",
+    "        d_1\\\\\n",
+    "        \\vdots\\\\\n",
+    "        d_{N}\n",
+    "    \\end{bmatrix}\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "k5SdPFYbjSTR0Unuprld",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "### Adding Noise\n",
+    "\n",
+    "Observational data are always contaminated with noise. Here we add Gaussian noise $N(0,\\epsilon)$ (zero mean and standard deviation $\\sigma$). Here we choose \n",
+    "\n",
+    "$$ \\epsilon = \\% |d| + \\text{floor} $$\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'LinearInversionDirectApp' object has no attribute 'interact_plot_data'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-6d6bcf7ec2cf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mQ_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteract_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m: 'LinearInversionDirectApp' object has no attribute 'interact_plot_data'"
+     ]
+    }
+   ],
+   "source": [
+    "Q_data = app.interact_plot_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "Kfas1vohMX9B1w8haaeF",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "## Step4: All three steps together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "gpeg40wOdccGxZMMBIxU",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 3
+     },
+     "outputId": {
+      "block": "yFInpwVxV9MsRBuL5pNr",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 3
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "app.interact_plot_all_three_together()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "I5cRNN1g9FcrGvbH7UKL",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "## Inverse Problem\n",
+    "\n",
+    "In the inverse problem we attempt to find the model $\\mathbf{m}$ that gave rise to the observational data $\\mathbf{d}^{obs}$. The inverse problem is formulated as an optimization problem: \n",
+    "\n",
+    "\n",
+    "$$\\text{minimize} \\ \\ \\ \\phi(\\mathbf{m}) = \\phi_d(\\mathbf{m}) + \\beta \\phi_m(\\mathbf{m}) $$\n",
+    "\n",
+    "where \n",
+    "\n",
+    "- $\\phi_d$: data misfit\n",
+    "- $\\phi_m$: model regularization\n",
+    "- $\\beta$: trade-off (or Tikhonov) parameter  $0<\\beta<\\infty$\n",
+    "\n",
+    "Data misfit is defined as \n",
+    "\n",
+    "$$ \\phi_d = \\sum_{j=1}^{N}\\Big(\\frac{\\mathbf{g}_j\\mathbf{m}-d^{obs}_j}{\\epsilon_j}\\Big)^2$$\n",
+    "\n",
+    "where $\\epsilon_j$  is an estimate of the standard deviation of the $j$th datum.\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "DPEqdTQucMMV6G5ONR7c",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "The model regularization term, $\\phi_m$, can be written as \n",
+    "\n",
+    "$$ \\phi_m(\\mathbf{m}) = \\alpha_s \\int (\\mathbf{m}-\\mathbf{m}_{ref}) dx + \\alpha_x \\int (\\frac{d \\mathbf{m}}{dx}) dx$$\n",
+    "\n",
+    "The first term is referred to as the \"smallness\" term. Minimizing this generates a model that is close to a reference model $m_{ref}$. The second term penalizes roughness of the model. It is generically referred to as a \"flattest\" or \"smoothness\" term.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "j7hVPYIA73jbxv0bAinV",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 2
+     }
+    }
+   },
+   "source": [
+    "## Step5: Invert the data, and explore inversion results\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "8k53qHfi1hcwT29TMxNC",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 1
+     }
+    }
+   },
+   "source": [
+    "In the inverse problem we define parameters needed to evaluate the data misfit and the model regularization terms. We then deal with parameters associated with the inversion.\n",
+    "\n",
+    "### Parameters\n",
+    "\n",
+    "- `mode`: `Run` or `Explore`\n",
+    "    - `Run`: Each click of the app, will run `n_beta` times of inversion\n",
+    "    - `Explore`: Not running inversions, but explore result of the inversions\n",
+    "\n",
+    "\n",
+    "- `noise option`: `error contaminated` or `clean data`\n",
+    "\n",
+    "#### Misfit\n",
+    "- `percent`: percentage of the uncertainty (%)\n",
+    "\n",
+    "- `floor`: floor of the uncertainty (%)\n",
+    "\n",
+    "- `chifact`: chi factor for stopping criteria (when $\\phi_d^{\\ast}=N \\rightarrow$ `chifact=1`)\n",
+    "\n",
+    "#### Model norm\n",
+    "- `mref`: reference model\n",
+    "\n",
+    "- `alpha_s`: $\\alpha_s$ for smallness\n",
+    "\n",
+    "- `alpha_x`: $\\alpha_x$ for smoothness\n",
+    "\n",
+    "#### Beta\n",
+    "- `beta_min`: minimum $\\beta$\n",
+    "\n",
+    "- `beta_max`: maximum $\\beta$\n",
+    "\n",
+    "- `n_beta`: the number of $\\beta$\n",
+    "\n",
+    "#### Plotting options\n",
+    "\n",
+    "- `data`: `obs & pred` or `normalized misfit`\n",
+    "    - `obs & pred`: show observed and predicted data\n",
+    "    - `normalized misfit`: show normalized misfit\n",
+    "\n",
+    "\n",
+    "- `tikhonov`: `phi_d & phi_m` or `phi_d vs phi_m`\n",
+    "    - `phi_d & phi_m`: show $\\phi_d$ and $\\phi_m$ as a function of $\\beta$\n",
+    "    - `phi_d vs phi_m`: show tikhonov curve\n",
+    "    \n",
+    "- `i_beta`: i-th $\\beta$ value\n",
+    "\n",
+    "- `scale`: `linear` or `log`\n",
+    "    - `linear`: linear scale for plotting the third panel\n",
+    "    - `log`: log scale for plotting the third panel     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "iooxa": {
+     "id": {
+      "block": "zYsKlEza7On1cZcJUO1x",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 3
+     },
+     "outputId": {
+      "block": "XHsuSTVmGPtaRtV3WO9m",
+      "project": "VNMrkxzChhdveZyf6lmb",
+      "version": 3
+     }
+    },
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "app.interact_plot_inversion()"
+   ]
+  }
+ ],
+ "metadata": {
+  "iooxa": {
+   "id": {
+    "block": "lb7CgEnVPzfs79VcKpB1",
+    "project": "VNMrkxzChhdveZyf6lmb",
+    "version": 5
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/seismic/.ipynb_checkpoints/Seis_NMO-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/Seis_NMO-checkpoint.ipynb
similarity index 100%
rename from seismic/.ipynb_checkpoints/Seis_NMO-checkpoint.ipynb
rename to Notebooks/seismic/.ipynb_checkpoints/Seis_NMO-checkpoint.ipynb
diff --git a/seismic/.ipynb_checkpoints/Seis_Reflection-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/Seis_Reflection-checkpoint.ipynb
similarity index 100%
rename from seismic/.ipynb_checkpoints/Seis_Reflection-checkpoint.ipynb
rename to Notebooks/seismic/.ipynb_checkpoints/Seis_Reflection-checkpoint.ipynb
diff --git a/seismic/.ipynb_checkpoints/Seis_Refraction-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/Seis_Refraction-checkpoint.ipynb
similarity index 100%
rename from seismic/.ipynb_checkpoints/Seis_Refraction-checkpoint.ipynb
rename to Notebooks/seismic/.ipynb_checkpoints/Seis_Refraction-checkpoint.ipynb
diff --git a/Notebooks/seismic/.ipynb_checkpoints/Seis_VerticalResolution-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/Seis_VerticalResolution-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..18c102e67303882d9345dfe757d3db2e95ef4fe4
--- /dev/null
+++ b/Notebooks/seismic/.ipynb_checkpoints/Seis_VerticalResolution-checkpoint.ipynb
@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Seismic Resolution and Forward Modelling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the necessary packages\n",
+    "%matplotlib inline   \n",
+    "from geoscilabs.seismic.syntheticSeismogram import InteractSeismogramTBL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When referring to vertical resolution, the question to ask is: \"Can the two arrivals (one from the top, and one from the bottom of the layer) be distinguished?\" \n",
+    "\n",
+    "Adjust the layer thickness for the middle layer (by adjusting d2 and/or d3) and the frequency of the input pulse to investigate vertical resolution. You can also add noise to the trace. \n",
+    "\n",
+    "The geologic model is:\n",
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/seismic/geoModel.png?raw=true\" style=\"width: 50%; height: 50%\"></img>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parameters of the below widget are:\n",
+    "\n",
+    "- d2: depth of the interface between layer 1 and 2\n",
+    "- d3: depth of the interface between layer 2 and 3\n",
+    "- rho1: density of the layer 1 ($kg/m^3$)\n",
+    "- rho2: density of the layer 2 ($kg/m^3$)\n",
+    "- rho3: density of the layer 3 ($kg/m^3$)\n",
+    "- v1: velocity of the layer 1 ($m/s$)\n",
+    "- v2: velocity of the layer 2 ($m/s$)\n",
+    "- v3: velocity of the layer 3 ($m/s$)\n",
+    "- wavef: peak frequency of the Ricker wavelet\n",
+    "- waveA: amplitude of Ricker wavelet\n",
+    "- AddNoise: swith for adding noise \n",
+    "- usingT: switch for considering transmission coefficient for reflectivity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ec1451066644b66be4c8aa47088ebb6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=9.0, description='d2', min=1.0), FloatSlider(value=9.5, description='d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "InteractSeismogramTBL();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/seismic/.ipynb_checkpoints/SeismicApplet-checkpoint.ipynb b/Notebooks/seismic/.ipynb_checkpoints/SeismicApplet-checkpoint.ipynb
similarity index 100%
rename from seismic/.ipynb_checkpoints/SeismicApplet-checkpoint.ipynb
rename to Notebooks/seismic/.ipynb_checkpoints/SeismicApplet-checkpoint.ipynb
diff --git a/seismic/fourier_transform.ipynb b/Notebooks/seismic/.ipynb_checkpoints/fourier_transform-checkpoint.ipynb
similarity index 100%
rename from seismic/fourier_transform.ipynb
rename to Notebooks/seismic/.ipynb_checkpoints/fourier_transform-checkpoint.ipynb
diff --git a/Notebooks/seismic/2D-LinearInversion-Crosswell-Tomorgraphy.ipynb b/Notebooks/seismic/2D-LinearInversion-Crosswell-Tomorgraphy.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2f65fe53dda1974a2cc83fdbf2cc556737610899
--- /dev/null
+++ b/Notebooks/seismic/2D-LinearInversion-Crosswell-Tomorgraphy.ipynb
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2D Linear Inversion of Crosswell Tomography Data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from geoscilabs.inversion.TomographyInversion import TomographyInversionApp\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ipywidgets import interact, IntSlider\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Purpose\n",
+    "\n",
+    "From the Linear inversion notebook (1D), we learned important apsects about the linear inversion. \n",
+    "However, real world geophysical inverse problem are not often 1D, but multidimensional (2D or 3D), and this extension of dimension allows us to put more apriori (or geologic) information through the regularization term. \n",
+    "In this notebook, we explore these multidimensional aspects of the linear inversion by using 2D traveltime croswell tomography example. \n",
+    "\n",
+    "## Outline\n",
+    "This notebook includes four steps:\n",
+    "- Step1: Generate a velocity model\n",
+    "- Step2: Simulate traveltime data and add noise\n",
+    "- Step3: Run $l_2$ inversion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step1: Generate a velocity model\n",
+    "\n",
+    "Here we set up a velocity model using a following app. Controlling parameters of the app are:\n",
+    "\n",
+    "- `set mesh`: use active **only** when you want to change the 2D mesh\n",
+    "- `add block`: use active when you want to add block (if not stay inactive)\n",
+    "- `model type`: background or block\n",
+    "- `show grid?`: show grid of the mesh\n",
+    "\n",
+    "- `v0`: velocity of the background\n",
+    "- `v1`: velocity of the block\n",
+    "- `xc`: x center of the block\n",
+    "- `yc`: y center of the block\n",
+    "- `dx`: width of the block\n",
+    "- `dy`: thickness of the block\n",
+    "- `nx`: # of cells in x-direction (this is only active when `set_mesh=active`)\n",
+    "- `ny`: # of cells in y-direction (this is only active when `set_mesh=active`)\n",
+    "\n",
+    "### Changing # of cells in $x$- or $z$- direction\n",
+    "Related parameters for this task are: `set mesh`, `nx`, `ny`. \n",
+    "Size of the 2D domain are fixed to 200m $\\times$ 400m, but the number of cells in each direction can be changed such that you can alter size of the cells in each direction. When you change either `nx` or `ny` make sure you choose `set mesh=active` otherwise `set mesh=inactive`. Note that once mesh setup is changed, velocity model is reset to a background value (`v0`). \n",
+    "\n",
+    "### Changing a parameter of a single block\n",
+    "Although you can change the location, size, and velocity of the block there are few rules that you need to follow to do so. \n",
+    "\n",
+    "1. If you want to change the parameter of the block: first set `add block=active` then change parameters of the block (`v1`, `xc`, `zc`, `dx`, `dy`)\n",
+    "\n",
+    "2. Once you changed the parameters, make sure first choose `model type=background` then change that to `model type=block`\n",
+    "\n",
+    "### Adding more blocks\n",
+    "You can also add multiple blocks. To add a block follow below steps:\n",
+    "\n",
+    "1. Set `add block=inactive`, then change the parameter of the new block using: `v1`, `xc`, `zc`, `dx`, `dy`. Velocity model will not change, but you can see the white lines which illustrate boundary of the new block.\n",
+    "\n",
+    "2. Once you are happy with the new block, set `add block=active`, then velocity model will be updated with the new block that you have set. \n",
+    "\n",
+    "3. Repeat 1 and 2 if you want to add more blocks. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b68b953b597c4cf281679c6a5ab2b2fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(FloatSlider(value=1000.0, continuous_update=False, description='v0', max=3000.0,…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app = TomographyInversionApp()\n",
+    "app.interact_plot_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step2: Simulate travel time data and add noise\n",
+    "\n",
+    "Within this app, by using the velocity model set up, we simulate traveltime tomography data and add Gaussian noise. This syntehtic data set will be used in the following inversion module. Controlling parameters are:\n",
+    "\n",
+    "- `percent (%)`: percentage of the Gaussian noise\n",
+    "- `floor (s)`: floor of the Gaussain noise\n",
+    "- `random seed`: seed to generate random variables having normal distribution\n",
+    "- `tx_rx_plane` or `histogram`: choice of the plotting data\n",
+    "- `update`: this buttion is for the interactin between the first app. If the velocity model is changed by altering the first app, you can simply click `update` to run simulation again with the updated velocity model. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfb3a20158ab4e6b96ac1caa1a06f945",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Output(), Box(children=(Box(children=(BoundedFloatText(value=0.0, description='percent ($\\\\%$):…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app.interact_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step3: $l_2$ inversion\n",
+    "\n",
+    "- `maxIter`: maximum number of iteration\n",
+    "- `m0`: initial model\n",
+    "- `mref`: reference model\n",
+    "- `percentage`: percent standard deviation for the uncertainty\n",
+    "- `floor`: floor value for the uncertainty\n",
+    "- `chifact`: chifactor for the target misfit\n",
+    "- `beta0_ratio`: ratio to set the initial beta\n",
+    "- `coolingFactor`: cooling factor to cool beta\n",
+    "- `n_iter_per_beta`: # of interation for each beta value \n",
+    "- `alpha_s`: $\\alpha_s$\n",
+    "- `alpha_x`: $\\alpha_x$\n",
+    "- `alpha_z`: $\\alpha_z$\n",
+    "- `use_target`: use target misfit as a stopping criteria or not"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-1: Run inversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "\n",
+      "        SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+      "        ***Done using same Solver and solverOpts as the problem***\n",
+      "SimPEG.SaveOutputEveryIteration will save your inversion progress as: '###-InversionModel-2021-06-22-19-34.txt'\n",
+      "model has any nan: 0\n",
+      "=============================== Projected GNCG ===============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "x0 has any nan: 0\n",
+      "   0  2.39e+06  3.40e+02  4.00e-02  9.60e+04    1.41e-02      0              \n",
+      "   1  1.20e+06  6.76e+02  3.53e-06  6.81e+02    3.20e+05      0              \n",
+      "   2  5.98e+05  6.60e+02  1.37e-05  6.68e+02    3.15e+05      0              \n",
+      "   3  2.99e+05  6.30e+02  5.14e-05  6.45e+02    3.05e+05      0   Skip BFGS  \n",
+      "   4  1.50e+05  5.77e+02  1.83e-04  6.04e+02    2.87e+05      0   Skip BFGS  \n",
+      "   5  7.48e+04  4.94e+02  5.91e-04  5.38e+02    2.59e+05      0   Skip BFGS  \n",
+      "   6  3.74e+04  3.84e+02  1.66e-03  4.46e+02    2.17e+05      0   Skip BFGS  \n",
+      "   7  1.87e+04  2.66e+02  3.93e-03  3.40e+02    1.67e+05      0   Skip BFGS  \n",
+      "   8  9.35e+03  1.65e+02  7.79e-03  2.38e+02    1.18e+05      0   Skip BFGS  \n",
+      "   9  4.67e+03  9.27e+01  1.32e-02  1.54e+02    7.65e+04      0   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 9.6048e+03\n",
+      "1 : |xc-x_last| = 4.0147e-04 <= tolX*(1+|x0|) = 1.0141e-01\n",
+      "0 : |proj(x-g)-x|    = 7.6521e+04 <= tolG          = 1.0000e-10\n",
+      "0 : |proj(x-g)-x|    = 7.6521e+04 <= 1e3*eps       = 1.0000e-07\n",
+      "0 : maxIter   =      20    <= iter          =     10\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model, pred, save = app.run_inversion(\n",
+    "    maxIter=20,\n",
+    "    m0=1./1000.,\n",
+    "    mref=1./1250.,\n",
+    "    percentage=0,\n",
+    "    floor=0.01,\n",
+    "    chifact=1,\n",
+    "    beta0_ratio=1e2,\n",
+    "    coolingFactor=2,\n",
+    "    n_iter_per_beta=1,\n",
+    "    alpha_s=1/(app.mesh_prop.hx.min())**2 * 1e4,\n",
+    "    alpha_x=1,\n",
+    "    alpha_z=1,\n",
+    "    use_target=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-2: Plot recovered model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c858a8932b744d309ba953148fa07700",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, description='ii', max=10), Checkbox(value=False, description='fixed'…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app.interact_model_inversion(model, clim=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-3: Plot predicted data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a937a37991b94b9cac911daece8cc129",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=10, continuous_update=False, description='ii', max=10), Checkbox(value=F…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "app.interact_data_inversion(pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3-4: Plot misfit curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "save.plot_misfit_curves()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "save.plot_tikhonov_curves()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Notebooks/seismic/InversionModel-2021-06-17-14-51.txt b/Notebooks/seismic/InversionModel-2021-06-17-14-51.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3259043a6ae236eaf55b36f6e9c7c11b91eed55a
--- /dev/null
+++ b/Notebooks/seismic/InversionModel-2021-06-17-14-51.txt
@@ -0,0 +1,11 @@
+  #     beta     phi_d     phi_m   phi_m_small     phi_m_smoomth_x     phi_m_smoomth_y     phi_m_smoomth_z      phi
+   1 1.1963e+06 6.7640e+02 3.5279e-06 3.4919e-06 1.7688e-08 1.8347e-08  0.0000e+00  9.6047e+04
+   2 5.9817e+05 6.6019e+02 1.3666e-05 1.3526e-05 6.8431e-08 7.1515e-08  0.0000e+00  6.8062e+02
+   3 2.9908e+05 6.2997e+02 5.1361e-05 5.0832e-05 2.5664e-07 2.7214e-07  0.0000e+00  6.6836e+02
+   4 1.4954e+05 5.7710e+02 1.8264e-04 1.8074e-04 9.0978e-07 9.9172e-07  0.0000e+00  6.4533e+02
+   5 7.4771e+04 4.9419e+02 5.9126e-04 5.8495e-04 2.9386e-06 3.3657e-06  0.0000e+00  6.0441e+02
+   6 3.7386e+04 3.8417e+02 1.6637e-03 1.6451e-03 8.3255e-06 1.0325e-05  0.0000e+00  5.3840e+02
+   7 1.8693e+04 2.6622e+02 3.9335e-03 3.8849e-03 2.0414e-05 2.8144e-05  0.0000e+00  4.4637e+02
+   8 9.3464e+03 1.6468e+02 7.7947e-03 7.6821e-03 4.4344e-05 6.8183e-05  0.0000e+00  3.3975e+02
+   9 4.6732e+03 9.2702e+01 1.3211e-02 1.2975e-02 8.7874e-05 1.4814e-04  0.0000e+00  2.3753e+02
+  10 2.3366e+03 4.9649e+01 1.9637e-02 1.9182e-02 1.6175e-04 2.9389e-04  0.0000e+00  1.5444e+02
diff --git a/Notebooks/seismic/InversionModel-2021-06-22-19-34.txt b/Notebooks/seismic/InversionModel-2021-06-22-19-34.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3259043a6ae236eaf55b36f6e9c7c11b91eed55a
--- /dev/null
+++ b/Notebooks/seismic/InversionModel-2021-06-22-19-34.txt
@@ -0,0 +1,11 @@
+  #     beta     phi_d     phi_m   phi_m_small     phi_m_smoomth_x     phi_m_smoomth_y     phi_m_smoomth_z      phi
+   1 1.1963e+06 6.7640e+02 3.5279e-06 3.4919e-06 1.7688e-08 1.8347e-08  0.0000e+00  9.6047e+04
+   2 5.9817e+05 6.6019e+02 1.3666e-05 1.3526e-05 6.8431e-08 7.1515e-08  0.0000e+00  6.8062e+02
+   3 2.9908e+05 6.2997e+02 5.1361e-05 5.0832e-05 2.5664e-07 2.7214e-07  0.0000e+00  6.6836e+02
+   4 1.4954e+05 5.7710e+02 1.8264e-04 1.8074e-04 9.0978e-07 9.9172e-07  0.0000e+00  6.4533e+02
+   5 7.4771e+04 4.9419e+02 5.9126e-04 5.8495e-04 2.9386e-06 3.3657e-06  0.0000e+00  6.0441e+02
+   6 3.7386e+04 3.8417e+02 1.6637e-03 1.6451e-03 8.3255e-06 1.0325e-05  0.0000e+00  5.3840e+02
+   7 1.8693e+04 2.6622e+02 3.9335e-03 3.8849e-03 2.0414e-05 2.8144e-05  0.0000e+00  4.4637e+02
+   8 9.3464e+03 1.6468e+02 7.7947e-03 7.6821e-03 4.4344e-05 6.8183e-05  0.0000e+00  3.3975e+02
+   9 4.6732e+03 9.2702e+01 1.3211e-02 1.2975e-02 8.7874e-05 1.4814e-04  0.0000e+00  2.3753e+02
+  10 2.3366e+03 4.9649e+01 1.9637e-02 1.9182e-02 1.6175e-04 2.9389e-04  0.0000e+00  1.5444e+02
diff --git a/seismic/Seis_NMO.ipynb b/Notebooks/seismic/Seis_NMO.ipynb
similarity index 99%
rename from seismic/Seis_NMO.ipynb
rename to Notebooks/seismic/Seis_NMO.ipynb
index 37209ca86be3768414bf6a648b10daf608937105..0b96c72d709b8a981fda4c6e36c0467c439c4f4a 100644
--- a/seismic/Seis_NMO.ipynb
+++ b/Notebooks/seismic/Seis_NMO.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -168,13 +168,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ab3fd9c34cdf42ad875deb54e14d6c2d",
+       "model_id": "43a9862229f44dd3926af983f2d4b8d0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -210,13 +210,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbdb2fe92e5440b88a565d3b30b47228",
+       "model_id": "6c48406c9ab8474689af93c7c954a8ab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
diff --git a/seismic/Seis_Reflection.ipynb b/Notebooks/seismic/Seis_Reflection.ipynb
similarity index 99%
rename from seismic/Seis_Reflection.ipynb
rename to Notebooks/seismic/Seis_Reflection.ipynb
index 45da57d45feed14393974fd3604ffff98ad79dcd..78a0bc787a4c9992c2e58279df409a6a7527f8b1 100644
--- a/seismic/Seis_Reflection.ipynb
+++ b/Notebooks/seismic/Seis_Reflection.ipynb
@@ -152,7 +152,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe4a877307f247ed89387159d0575b85",
+       "model_id": "02c58095c4ad4a3e9c4f8d4bf01c2970",
        "version_major": 2,
        "version_minor": 0
       },
@@ -191,7 +191,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7b67f595e0a4b7ebe279b40e59da28b",
+       "model_id": "8acc94ac2fe74ababc6bb7b89f4c2018",
        "version_major": 2,
        "version_minor": 0
       },
@@ -235,7 +235,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6e30d9df4e04ec0b803c6780d1147f3",
+       "model_id": "1a42473c00bd42d88fa7a7c40e9e427d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -312,7 +312,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "29534e192ee04e0bb3c7f74f127dbd99",
+       "model_id": "1af155fad1cc488a820695945b236f63",
        "version_major": 2,
        "version_minor": 0
       },
@@ -439,7 +439,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\tobia\\Desktop\\Uni\\_Python Scripts Allgemein\\geosci-labs-main-notebooks\\notebooks\\seismic/zeit.npy\n"
+      "C:\\Users\\tobia\\einfuehrung-in-die-geophysikalische-erkundung\\Notebooks\\seismic/zeit.npy\n"
      ]
     },
     {
@@ -497,7 +497,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "71675c9e172549668e660cb4f44c1381",
+       "model_id": "6d5e108a43fc4fe18c41d7a099798954",
        "version_major": 2,
        "version_minor": 0
       },
@@ -540,7 +540,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba6a7b777ed14de9b1eeb5db201ec944",
+       "model_id": "32ede16fe7bb44a985342ed5fd303aa3",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/Notebooks/seismic/Seis_Refraction.ipynb b/Notebooks/seismic/Seis_Refraction.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b2de02159b536b303332ed5722dfb2195ba26fd8
--- /dev/null
+++ b/Notebooks/seismic/Seis_Refraction.ipynb
@@ -0,0 +1,297 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from geoscilabs.seismic.SeismicRefraction import (\n",
+    "    plotWavelet, viewTXdiagram, plotWiggleTX, makeinteractSeisRefracSurvey,\n",
+    "    makeinteractTXwigglediagram\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interpretation and data acquisition strategies of seismic refraction data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the <a href=\"https://www.3ptscience.com/app/SeismicRefraction\">3pt Science app</a>, you explored the expected arrival times for refractions and reflections from a two-layer over a half-space model. \n",
+    "\n",
+    "In this notebook, we will use synthetic seismic data to examine the impact of survey parameters on the expected seismic data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Source "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In an ideal case, the source wavelet would be an impulse (ie. an instantaneous spike). However, in reality, the source energy is spread in space and in time (see the <a href=\"http://gpg.geosci.xyz/content/seismic/wave_basics.html#waves-and-rays\">GPG: Waves and Rays</a>). The source wavelet used for these examples is shown below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 216x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Source Wavelet'}, xlabel='Signal Amplitude', ylabel='time (s)'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plotWavelet()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "Below, we show 3 plots:\n",
+    "- **left**: expected arrival times for the direct, refracted waves and reflection from the first layer\n",
+    "- **center**: clean data - the wavelet arriving at the expected arrival time. Each line represents what would be recorded by an ideal geophone.\n",
+    "- **right**: noisy data - clean data + random noise. \n",
+    "\n",
+    "The model used is the same as is in the lab write-up: \n",
+    "- v1 = 400 m/s\n",
+    "- v2 = 1000 m/s\n",
+    "- v3 = 1500 m/s\n",
+    "- z1 = 5m (depth to layer 1)\n",
+    "- z2 = 15m (depth to layer 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(15,6))\n",
+    "ax[0].set_title('Expected Arrival Times')\n",
+    "ax[1].set_title('Clean Data')\n",
+    "ax[2].set_title('Noisy Data')\n",
+    "ax[0]=viewTXdiagram(x0=1., dx=8, v1=400., v2=1000., v3=1500., z1=5., z2=15., ax=ax[0])\n",
+    "ax[1]=plotWiggleTX(x0=1., dx=8, v1=400., v2=1000., v3=1500., z1=5., z2=15., ax=ax[1])\n",
+    "ax[2]=plotWiggleTX(x0=1., dx=8, v1=400., v2=1000., v3=1500., z1=5., z2=15., ax=ax[2], noise=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup for the seismic refraction survey"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider a shot gather for seismic refraction survey, which means we have one shot (source), and multiple receivers (12). Shot location is fixed at x=0. There are two survey parameters: \n",
+    "\n",
+    "- x0: offset between shot and the first geophone\n",
+    "- dx: spacing between two consecutive geophones\n",
+    "\n",
+    "In the widget below you can alter x0 or dx to change your survey setup. Run the next cell then try to change x0 and dx in the cell below that. Note that the next two cells are designed to help you visualize the survey layout. The x0 and dx parameter adjustment sliders here are not linked to the widget at the end of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbb07e5d8f7940838bad1d30f63a799d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='x0', max=10), IntSlider(value=8, description='dx', max=1…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "makeinteractSeisRefracSurvey()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpretation of seismic refraction data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assume that you have seismic refraction data. The structure of the earth is unknown and you may want to obtain useful information about the subsurface. We will assume that the subsurface in the survey area has a three-layer structure and that the velocities increase with depth. \n",
+    "Thus, there can be four unknowns:\n",
+    "\n",
+    "- v1: velocity of the first layer (m/s)\n",
+    "- v2: velocity of the second layer (m/s)\n",
+    "- v3: velocity of the third layer (m/s)\n",
+    "- z1: depth of the first layer (m)\n",
+    "- z2: depth of the second layer (m)\n",
+    "\n",
+    "Based on the above information, we may expect to have up to four arrivals at a geophone, related to \n",
+    "\n",
+    "- Direct\n",
+    "- Reflected: interface 1\n",
+    "- Refraction: interface 1\n",
+    "- Refraction: interface 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The widget below will allow you to estimate the layer depths and velocities. The parameters for the widget are:\n",
+    "\n",
+    "- x0: offset between shot and the first geophone\n",
+    "- dx: spacing between two consecutive geophones\n",
+    "- Fit: checking this activates fittting function (if you click this red line will show up)\n",
+    "- tI: intercept time for a line function (s)\n",
+    "- v: inverse slope of the line function (m/s; which can be velocity of either direct and critically refracted wave)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run below widget and find useful subsurface information!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0c9443284824c85855acdb4fd904cde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=4, description='x0', max=10, min=1), IntSlider(value=4, description='dx'…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "makeinteractTXwigglediagram()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "latex_envs": {
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 0
+  },
+  "widgets": {
+   "state": {
+    "58141af61d2a4d6393c0f5e35a09cccf": {
+     "views": [
+      {
+       "cell_index": 10
+      }
+     ]
+    },
+    "75727a01f50445469ade2c7092094a5b": {
+     "views": [
+      {
+       "cell_index": 15
+      }
+     ]
+    }
+   },
+   "version": "1.2.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/seismic/Seis_VerticalResolution.ipynb b/Notebooks/seismic/Seis_VerticalResolution.ipynb
similarity index 79%
rename from seismic/Seis_VerticalResolution.ipynb
rename to Notebooks/seismic/Seis_VerticalResolution.ipynb
index ea4e65c48510723c8bdd4922220ebf125087ca0e..ae75a6b91f85b5d8b955ba629b8d1f2917719ff5 100644
--- a/seismic/Seis_VerticalResolution.ipynb
+++ b/Notebooks/seismic/Seis_VerticalResolution.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
     "Adjust the layer thickness for the middle layer (by adjusting d2 and/or d3) and the frequency of the input pulse to investigate vertical resolution. You can also add noise to the trace. \n",
     "\n",
     "The geologic model is:\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/seismic/geoModel.png?raw=true\" style=\"width: 50%; height: 50%\"></img>\n"
+    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/seismic/geoModel.png?raw=true\" style=\"width: 50%; height: 50%\"></img>\n"
    ]
   },
   {
@@ -52,9 +52,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a28d8c0796884516afa0eedb9d041a7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=9.0, description='d2', min=1.0), FloatSlider(value=9.5, description='d…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "InteractSeismogramTBL();"
    ]
@@ -84,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,
diff --git a/seismic/SeismicApplet.ipynb b/Notebooks/seismic/SeismicApplet.ipynb
similarity index 96%
rename from seismic/SeismicApplet.ipynb
rename to Notebooks/seismic/SeismicApplet.ipynb
index d973a6cec20ca93b2c046c59d432588d6a60dd3f..8676a31feac5d0cc18a344eaa9866da4649a44c1 100644
--- a/seismic/SeismicApplet.ipynb
+++ b/Notebooks/seismic/SeismicApplet.ipynb
@@ -35,7 +35,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d83e8275a0ab4e38bc2d00f300f1929e",
+       "model_id": "413a452191f141fb8e08f7b9d5ced268",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/Notebooks/seismic/fourier_transform.ipynb b/Notebooks/seismic/fourier_transform.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aa51756d1041190978e8c3843eb574433b8736ba
--- /dev/null
+++ b/Notebooks/seismic/fourier_transform.ipynb
@@ -0,0 +1,518 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style='background-image: url(\"../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n",
+    "    <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px\">\n",
+    "        <div style=\"position: relative ; top: 50% ; transform: translatey(-50%)\">\n",
+    "            <div style=\"font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.8) ; line-height: 100%\">Signal Processing</div>\n",
+    "            <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\">Fourier Transformation - Solution</div>\n",
+    "        </div>\n",
+    "    </div>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seismo-Live: http://seismo-live.org\n",
+    "\n",
+    "##### Authors:\n",
+    "* Stefanie Donner ([@stefdonner](https://github.com/stefdonner))\n",
+    "* Celine Hadziioannou ([@hadzii](https://github.com/hadzii))\n",
+    "* Ceri Nunn ([@cerinunn](https://github.com/cerinunn))\n",
+    "\n",
+    "<br>\n",
+    "Some code used in this tutorial is taken from [stackoverflow.com](http://stackoverflow.com/questions/4258106/how-to-calculate-a-fourier-series-in-numpy/27720302#27720302). We thank [Giulio Ghirardo](https://www.researchgate.net/profile/Giulio_Ghirardo) for his kind permission to use his code here.\n",
+    "\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Tutorial on Fourier transformation in 1D </h1>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Cell 0 - Preparation: load packages, set some basic options  \n",
+    "\n",
+    "%matplotlib inline\n",
+    "from scipy import signal\n",
+    "from obspy.signal.invsim import cosine_taper \n",
+    "from matplotlib import rcParams\n",
+    "import numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "plt.style.use('ggplot')\n",
+    "plt.rcParams['figure.figsize'] = 15, 3\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Fourier transformation\n",
+    "\n",
+    "In the world of seismology, we use the *Fourier transformation* to transform a signal from the time domain into the frequency domain. That means, we split up the signal and separate the content of each frequency from each other. Doing so, we can analyse our signal according to energy content per frequency. We can extract information on how much amplitude each frequency contributes to the final signal. In other words: we get a receipt of the ingredients we need to blend our measured signal. \n",
+    "\n",
+    "The *Fourier transformation* is based on the *Fourier series*. With the *Fourier series* we can approximate an (unknown) function $f(x)$ by another function $g_n(x)$ which consists of a sum over $N$ basis functions weighted by some coefficients. The basis functions need to be orthogonal. $sin$ and $cos$ functions seem to be a pretty good choice because any signal can be filtered into several sinusoidal paths. In the period range of $[-T/2 ; T/2]$ the *Fourier series* is defined as:\n",
+    "\n",
+    "$$\n",
+    "f(t) \\approx g_n(t) = \\frac{1}{2} a_0 + \\sum_{k=1}^N \\left[ a_k \\cos \\left(\\frac{2\\pi k t}{T} \\right) + b_k \\sin\\left(\\frac{2\\pi k t}{T}\\right)\\right]\n",
+    "$$\n",
+    "\n",
+    "$$ \n",
+    "a_k = \\frac{2}{T} \\int_{-T/2}^{T/2} f(t) \\cos\\left(\\frac{2\\pi k t}{T}\\right)dt\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "b_k = \\frac{2}{T} \\int_{-T/2}^{T/2} f(t) \\sin\\left(\\frac{2\\pi k t}{T}\\right)dt\n",
+    "$$\n",
+    "\n",
+    "At this stage, we consider continuous, periodic and infinite functions. The more basis functions are used to approximate the unknown function, the better is the approximation, i.e. the more similar the unknown function is to its approximation. \n",
+    "\n",
+    "For a non-periodic function the interval of periodicity tends to infinity. That means, the steps between neighbouring frequencies become smaller and smaller and thus the infinite sum of the *Fourier series* turns into an integral and we end up with the integral form of the *Fourier transformation*:\n",
+    "\n",
+    "$$\n",
+    "F(\\omega) = \\frac{1}{2\\pi} \\int_{-\\infty}^{\\infty} f(t) e^{-i\\omega t} dt \\leftrightarrow f(t) =  \\int_{-\\infty}^{\\infty} F(\\omega)e^{i\\omega t}dt\n",
+    "$$\n",
+    "\n",
+    "Attention: sign and factor conventions can be different in the literature!\n",
+    "\n",
+    "In seismology, we do not have continuous but discrete time signals. Therefore, we work with the discrete form of the *Fourier transformation*:\n",
+    "\n",
+    "$$\n",
+    "F_k = \\frac{1}{N} \\sum_{j=0}^{N-1} f_j e^{-2\\pi i k j /N} \\leftrightarrow f_k = \\sum_{j=0}^{N-1} F_j e^{2\\pi i k j /N}\n",
+    "$$\n",
+    "\n",
+    "Some intuitive gif animations on what the *Fourier transform* is doing, can be found [here](https://en.wikipedia.org/wiki/File:Fourier_series_and_transform.gif), [here](https://en.wikipedia.org/wiki/File:Fourier_series_square_wave_circles_animation.gif), and [here](https://en.wikipedia.org/wiki/File:Fourier_series_sawtooth_wave_circles_animation.gif).\n",
+    "Further and more detailed explanations on *Fourier series* and *Fourier transformations* can be found [here](https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/) and [here](www.fourier-series.com).\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Fourier series and its coefficients \n",
+    "\n",
+    "In the following two code cells, we first define a function which calculates the coefficients of the Fourier series for a given function. The function in the next cell does it the other way round: it is creating a function based on given coefficients and weighting factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "code_folding": [
+     1
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Cell 1: code by Giulio Ghirardo  \n",
+    "def fourier_series_coeff(f, T, N):\n",
+    "    \"\"\"Calculates the first 2*N+1 Fourier series coeff. of a periodic function.\n",
+    "\n",
+    "    Given a periodic, function f(t) with period T, this function returns the\n",
+    "    coefficients a0, {a1,a2,...},{b1,b2,...} such that:\n",
+    "\n",
+    "    f(t) ~= a0/2+ sum_{k=1}^{N} ( a_k*cos(2*pi*k*t/T) + b_k*sin(2*pi*k*t/T) )\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    f : the periodic function, a callable like f(t)\n",
+    "    T : the period of the function f, so that f(0)==f(T)\n",
+    "    N_max : the function will return the first N_max + 1 Fourier coeff.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    a0 : float\n",
+    "    a,b : numpy float arrays describing respectively the cosine and sine coeff.\n",
+    "    \"\"\"\n",
+    "    # From Nyquist theorem we must use a sampling \n",
+    "    # freq. larger than the maximum frequency you want to catch in the signal. \n",
+    "    f_sample = 2 * N\n",
+    "    \n",
+    "    # We also need to use an integer sampling frequency, or the\n",
+    "    # points will not be equispaced between 0 and 1. We then add +2 to f_sample.\n",
+    "    t, dt = np.linspace(0, T, f_sample + 2, endpoint=False, retstep=True)\n",
+    "    y = np.fft.rfft(f) / t.size\n",
+    "    y *= 2\n",
+    "    return y[0].real, y[1:-1].real[0:N], -y[1:-1].imag[0:N]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "code_folding": [
+     1
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Cell 2: code by Giulio Ghirardo  \n",
+    "def series_real_coeff(a0, a, b, t, T):\n",
+    "    \"\"\"calculates the Fourier series with period T at times t,\n",
+    "       from the real coeff. a0,a,b\"\"\"\n",
+    "    tmp = np.ones_like(t) * a0 / 2.\n",
+    "    for k, (ak, bk) in enumerate(zip(a, b)):\n",
+    "        tmp += ak * np.cos(2 * np.pi * (k + 1) * t / T) + bk * np.sin(\n",
+    "            2 * np.pi * (k + 1) * t / T)\n",
+    "    return tmp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---  \n",
+    "Now, we can create an arbitrary function, which we use to experiment with in the following example.   \n",
+    "1) When you re-run cell 3 several times, do you always see the same function? Why? What does it tell you about the Fourier series?  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cell 3: create periodic, discrete, finite signal\n",
+    "\n",
+    "# number of samples (intial value: 3000)\n",
+    "samp = 3000\n",
+    "# sample rate (initial value: 1)\n",
+    "dt = 1\n",
+    "# period\n",
+    "T = 1.0 / dt\n",
+    "length = samp * dt\n",
+    "# number of coefficients (initial value: 100)\n",
+    "N = 100\n",
+    "# weighting factors for coefficients (selected randomly)\n",
+    "a0 = np.random.rand(1)\n",
+    "a = np.random.randint(1, high=11, size=N)\n",
+    "b = np.random.randint(1, high=11, size=N)\n",
+    "\n",
+    "t = np.linspace(0, length, samp)             # time axis\n",
+    "sig = series_real_coeff(a0, a, b, t, T)\n",
+    "\n",
+    "# plotting\n",
+    "plt.plot(t, sig, 'r', label='arbitrary, periodic, discrete, finite signal')\n",
+    "plt.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
+    "plt.xlabel('time [sec]')\n",
+    "plt.ylabel('amplitude')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---  \n",
+    "Now, we can play with the signal and see what happens when we try to reconstruct it with a limited number of coefficients.  \n",
+    "2) Run the cells 4 and 5. What do you observe?  \n",
+    "3) Increase the number of coefficients $n$ step by step and re-run cells 4 and 5. What do you observe now? Can you explain?   \n",
+    "4) In cell 5 uncomment the lines to make a plot which is not normalized (and comment the other two) and re-run the cell. What do you see now and can you explain it?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coefficient a0 =  273\n",
+      "array coefficients ak = [1084 2337 2338  584 1091]\n",
+      "array coefficients bk = [1003 2002 2500 1007 1756]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cell 4: determine the first 'n' coefficients of the function using the code function of cell 1\n",
+    "T = 1        # period\n",
+    "n = 5        # number of coeffs to reconstruct\n",
+    "a0, a, b = fourier_series_coeff(sig, T, n)\n",
+    "a_ = a.astype(int)\n",
+    "b_ = b.astype(int)\n",
+    "print('coefficient a0 = ', int(a0))\n",
+    "print('array coefficients ak =', a_)\n",
+    "print('array coefficients bk =', b_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rKJ+U/YTll5dz4tGJzA5HEARBEARBENIk3hzPtJPTqJmrJnXd3gBAEj2KQnqRtFqAHDVOMVH/iv0J0AUwcv9IbJIts8MRBEEQBEEQhFT75dwvhOpD+brK18gSO31EQ1FIL1JC93ROmSIjKXe1O8OrDudEyAlWXlmZ2eEIgiAIgiAIQqpEm6KZfXo2DfM1pEpgFWQmk/0B0VAU0ouk0wE5s0cR4J1i71ApoBLjD48n2hSd2eEIgiAIgiAIwnPNOzOPSGMkQyoPsd8hGopCusvBPYoAcpmc72t+T6g+lKknpmZ2OIIgCIIgCILwTOGGcOadmUfLQi0p61cW4HHqqShmI6SXxDGK5NAeRYDy/uV5r/h7/HL2F25F38rscARBEARBEAThqeacnkO8OZ5Bbw56fKfoURTSm2OMYg5uKAJ8XflrFHIF4w6Py+xQBEEQBEEQBCFFEYYIfj3/K22KtKG4d3HH/Y4xiqJHUUgvObmYTVK5XHPRp1wfNt7YyJHgI5kdjiAIgiAIgiAks/DcQuLMcfSr0M/p/sSGoqRSZUZYL0U0FLOonDw9xpM+K/cZQS5BfHvwWzFdhiAIgiAIgpClRJuiWXh2IW8VfIuSPiWdH8zG02MoMzuARCdPnmTRokXYbDYaNWpE27ZtnR4/d+4cEydOJCAgAIBq1arx7rvvpmrd7Ej0KD7monLh6ypfM3DXQDZc20Dbom0zOyRBEARBEARBAGDRuUVEm6LpX7F/sseyc+pplmgo2mw2Fi5cyP/+9z98fX0ZNmwYlStXJm/evE7LlSpViqFDh77QutmN6FF09l4xe1GbcUfG0axgM3RKXWaHJAiCIAiCIORwceY45p+ZT6N8jXjD741kjzvNoyhJrzi6l5Om1FOLxcKFCxfYv38/AAaDAUM6NGSuXr1KUFAQgYGBKJVKatasyZEjqRuP9jLrZmmJDUXRowjYp8v4pvo33Iu9x4KzCzI7HEEQBEEQBEHg9wu/E2GMSLE3EXicepoNexRT3VC8ffs2/fv3Z+7cucyePRuA8+fPO/7/MsLDw/H19XX87evrS3h4eLLlLl++zODBgxk3bhx37txJ07rZjWN6DNFQdKiVuxbNCjRj+snphMSHZHY4giAIgiAIQg6mt+iZc3oOdfLU4c3AN1NcxtHp8zqPUZw/fz4dOnSgbt26fPTRRwCULl2auXPnvnQQUgrdsDKZzOnvQoUKMWvWLLRaLcePH2fSpElMmzYtVesm2r59O9u3bwdgwoQJ+Pn5vXTs6U2pVNrjSrjq4CaX45IF48wsk5tNpuKCisw4P4OZzWdmdjjpyrHvhRxH7PucSez3nEvs+5xL7PvXy4wjMwjRh7Cs/rKn7ldFQrVTpU6HXzZrLKa6oXj37l3q1KnjdJ9Wq8WUmHf7Enx9fQkLC3P8HRYWhre3t9MyLi4ujv9XqlSJhQsXEh0dnap1EzVu3JjGjRs7/g4NDX3p2NObn5+fPS6zmdxAfHg4sVkwzszigw/dSnXjl5O/0LFwx+SVpbIxx74Xchyx73Mmsd9zLrHvcy6x718fRquRSQcmUT2oOqVcSj11v7pHRuKmVmOxWrPkvs+dO/dTH0t16qm/vz/Xr193ui9xfODLKlKkCA8ePODRo0dYLBb2799P5cqVnZaJjIx09B5evXoVm82Gu7t7qtbNllQqJIVCFLNJwZeVvsRd5c6YQ2MyOxRBEARBEAQhB1p9ZTXBccFPH5uYQGYyZcs5FCENPYodOnRgwoQJNGnSBIvFwtq1a/nnn3/o1avXSwehUCjo0aMHY8eOxWaz0aBBA/Lly8e2bdsAaNq0KQcPHmTbtm0oFArUajVffvklMpnsqeu+DiSNRhSzSYGP1ocvK33Jtwe/ZeednTTI1yCzQxIEQRAEQRByCJtkY/bp2bzh9wZ18tR55rIysxkpm6WcJpJJKQ3ye4rr16+zY8cOQkJC8PX1pXHjxhQuXDgj48tQ9+/fz+wQkkmakhD4xhsYWrUiavz4TI4q6zFZTTRY1QCNQsO29ttQyrPETC8vRaSj5Fxi3+dMYr/nXGLf51xi378ettzcwsf/fMyshrN4u8jbz1zWc/BgtP/+i/X27Sy575+Vepqms+vChQtn64ZhtiN6FJ9KrVAzouoIPt3+KcsuLaNrqa6ZHZIgCIIgCILwmpMkiZmnZpLfPT8tC7V87vIykynb9ig+s6G4YsWKVG2kQ4cO6RKM4EzSasX0GM/wVsG3qBZUjUlHJ9G2SFvc1e6ZHZIgCIIgCILwGjvy8AjHHx1nbM2xqcpok5lMkE3HKD6zmE1YWJjj9uDBA9atW8fZs2cJDg7m7NmzrFu3jgcPHryqWHMcSasVxWyeQSaTMar6KMIMYcw4NSOzwxEEQRAEQRBec7NPz8Zb402HEqnsKMvGYxSf2Qzu06eP4/8///wz/fv3p3r16o77Dh06xIEDBzIuuhxOFLN5vvL+5Xmn6DvMPzOfriW7ktc9b2aHJAiCIAiCILyGrkRcYdutbXxV6St0Sl2q1snOqaepnh7jxIkTVK1a1em+KlWqcOLEiXQPSrATPYqpM6TKEGTIGH9EFP0RBEEQBEEQMsac03PQKrR0L9M91eu8tqmnSQUFBbFlyxan+7Zu3Zou8ygKKZM0GtFQTIU8bnnoVa4X666t49jDY5kdjiAIgiAIgvCaCY4LZvXV1XxQ4gN8tD6pX/F1TT1N6rPPPmPy5Mls2LABHx8fwsPDUSgUfPXVVxkZX44mabXIHj3K7DCyhb7l+7Ls4jK+Pfgt69usRyaTZXZIgiAIgiAIwmti4dmFWCUrPd/omab1ZEYjNg8PsuOZaaobioUKFWLq1KlcvnyZyMhIvLy8KF68OEpl9p+/LqsSqaep56py5evKXzNozyA23thI68KtMzskQRAEQRAE4TUQbYrm9wu/06pQKwp4FEjbymZztk09TVMrT6lUUrp06YyKRXiCpNMh0+szO4xs4/3i77Pw3ELGHR5Hk/xN0Cq1mR2SIAiCIAiCkM0tvbCUGHMMvcv1TvO6spyQetq799PfmNmzZ6dLMMITRI9imijkCr6p/g0dN3dk0blF9C6f9i+zIAiCIAiCICQyWo0sOLuA2rlrU86/XJrXlxmNr39D8YsvvnD6OyIigs2bN1OrVq10D0qwE6mnaVc3T10a5WvE1BNTeb/4+/jqfDM7JEEQBEEQBCGbWnd1HcHxwfxU76cX24DZDNm0oZjqqqelS5d2utWqVYvBgwezc+fOjIwvR5N0OntDUZKc7lfcuYPqmKju+TQjq40k3hLPT8df8AstCIIgCIIg5Hg2ycbs07Mp7VOaunnqvtA2ZCYTUjYdo5jqhmJKlEolj0RVzgwjaRPG2D3Rq+jXsiX+bdogv38/E6LK+op5F6Nrqa78fuF3rkRcyexwBEEQBEEQhGxo++3tXIm8Qp/yfV64on6OGKO4YsUKp7+NRiMnTpygYsWK6R6UYJfYUJTp9Ug6HQDy0FAUYWEAaPbsQd+hQ6bFl5UNrDSQNVfXMObQGBY3X5zZ4QiCIAiCIAjZzOxTs8njlodWhVu9+EaMxtc/9TQsLMzpZjabadWqFX379s3I+HK0xMZh0nGKqhMnHv//8uVXHlN24avzpX/F/vx7519239ud2eEIgiAIgiAI2cjRh0c5/PAwPd/oiUr+4qmjMrM526aeprpHsVOnTnh5eSW7P3FOxZd18uRJFi1ahM1mo1GjRrRt29bp8T179rB+/XoAtFotn3zyCQULFgSgb9++aLVa5HI5CoWCCRMmvHQ8WYGjRzFJQ1Fx9y4AVj8/FNevZ0pc2cVHZT7it/O/8d3B79jabisKuSKzQxIEQRAEQRCygTmn5+Cl8aJjiY4vvhGbDZnFgqTRpF9gr1CqexT79++f4v0DBgx46SBsNhsLFy5k+PDhTJkyhX379nE3oUGUKCAggNGjRzN58mTeeecd5s2b5/T4qFGjmDRp0mvTSATn1NNEitBQJLkcU+XKKK9dy6zQsgWNQsPwqsO5EH6B5ZeXZ3Y4giAIgiAIQjZwNfIqW25uoVvpbriqXF98QyaT/d9s2qOY6oai9ETlTYD4+Hjk8peqhwPA1atXCQoKIjAwEKVSSc2aNTly5IjTMiVKlMDNzQ2AYsWKEZYwTu91llLqqTw0FJuPD9YCBVDeu5esIqrgrFWhVlQLqsaEIxOINEZmdjiCIAiCIAhCFjfvzDzUCjU9yvR4qe3IzGaA17eYTe/e9knLTSaT4/+JYmNj02UexfDwcHx9H8935+vry5UrT69WuWPHjmRFdMaOHQtAkyZNaNy4cYrrbd++ne3btwMwYcIE/Pz8Xjb0dKdUKh1xyQIDAfDSaJAS7lPGxEBAANoiRZAZDPgpleDtnWnxZgczWsyg2qJqzDg3g5+b/pzZ4TxV0n0v5Cxi3+dMYr/nXGLf51xi32d9wbHBrLyykm7lulEyX8mX21hCh46rtzfybLjvn9tQ/OKLL5AkifHjx/PFF184Pebl5UXu3LlfOoiUeiufVoL27Nmz7Ny5k++++85x35gxY/Dx8SEqKorvv/+e3LlzU7p06WTrNm7c2KkRGRoa+tKxpzc/Pz9HXCqTCX8gOjgYY8J9vg8egJcXcW5u+ACR585hKfmSH+LXXG5FbrqV6sbc43NpV6AdZXzLZHZIKUq674WcRez7nEns95xL7PucS+z7rG/SkUmYrWa6Fev20vtKHhxMEBBrMuFisWTJff+sttxzG4qJDa6FCxeiyaCBmL6+vk6ppGFhYXin0Et269Yt5s6dy7Bhw3B3d3fc7+PjA4CnpydVqlTh6tWrKTYUs5sUi9mEhmIuWxZbrlz2vx8+FA3FVBhUeRDrr6/nf/v+x5rWa154LhxBEDKOJEnEmmMx28zYJBtymRwPtQdKearrrgmCIAjCC4s1xbL4/GJaFGpBIc9CL7291zr1dM2aNbRv3x6AdevWPXW5Di85l1+RIkV48OABjx49wsfHh/3799OvXz+nZUJDQ5k8eTKff/65U8vXYDAgSRI6nQ6DwcDp06d59913XyqerCKlhqI8LAyrnx/WhLRUeXBwpsSW3XhpvBheZTiD9gxi7bW1tC/aPrNDEoQcy2AxcCLkBGdDz3Ip4hKXIy4THB9MSHwIJpsp2fKuKlcCXQIp7FmYwp6FKetbliqBVcjnnk9c9BEEQRDSzdKLS4k2RdO7XO/nL5wKssRiNq9jQ/HJXr6MolAo6NGjB2PHjsVms9GgQQPy5cvHtm3bAGjatCmrVq0iNjaWBQsWONaZMGECUVFRTJ48GQCr1Urt2rWpUKFChsX6KjmK2SRWPTWZkEdF2YvZBAQAoBANxVTrUKIDSy4uYczBMTTJ3wR3tfvzVxIEIV1cjrjM5hub2Xt/L8cfHcdoNQLgo/WhhHcJauSqQYAuAF+dLyq5CoVcgdVmJdoUTaQxkgdxD7gedZ299/ZisNovngW6BFI/b32aFmhK3Tx1cVG5ZOZLFF4DJquJUH0oMaYYos3R2Gw2lHIlaoUaH60P/jp/1IrsecInCMKzmawm5p+dT41cNagYUPH5K6Rqo/aG4mvZo/jpp586/t+nT58MDaRSpUpUqlTJ6b6mTZs6/v/ZZ5/x2WefJVsvMDCQSZMmZWhsmeXJHkV5QmPd5ucHOh02Ly8UDx9mWnzZjVwmZ2ytsbRa14qfT/zMyGojMzskQXit3Y25y/LLy/nr+l9cjbyKDBll/crSvXR3auSqQQX/Cvi7+Kdpm1ablUsRlzjy8AgHHxzk75t/s+LyCrQKLa0Kt6JjiY5UC6omehqF53oQ94Djj45z4tEJzoWd42b0Te7G3sUm2Z65nq/Wl+LexSnhXYLSvqWpFlSNIp5FxGdOELK59dfW8yDuARPrTEy3bSb2KErZdHqMZzYUH6ayERKYkAYppK8n51F0aigC1qAgkXqaRhX8K9CxREcWnFlAh+IdKO5dPLNDEoTXitVm5d87//L7hd/ZeWcnADVy1eCjMh/xVsG3CHR5ud8LhVxBad/SlPYtTbfS3TDbzBx8cJC/rv/F+mvrWXVlFYU9C/NJ2U94v/j76JS69HhZwmvAJtk49ugY229tZ/vt7VyMuAiAWq6mlE8pKgVUon3R9gS5BuGh9sBD7YFCpsBsM2O2mQkzhPEo/hH3Yu9xOeIyq6+s5tfzvwIQoAugZu6aNCvQjEb5G73cvGuCILxykiQx+/RsSnqXpEHeBum23cQxiq9l6umT4wSfZsWKFekSjPAEjQZJJnP0KCqebCgGBqJ49CjTwsuuhlYZyuabmxmyZwirW69GLnv5uUAFIaczWU2subqGmadmcj3qOkEuQfSv2J+OJTqS1z1vhj2vSq6iTp461MlTh9HVR7PpxiZ+O/8bw/cN56fjP9GjTA96lOkhUs1zsNvRt1l5ZSUrL6/kTuwdlDIlVYOqMrLaSKoGVaWMbxk0irQX65MkietR1zkYfJAD9w+w+95u1l1bh0ahoUHeBnQo0YGG+RqKYkyCkA3suLODSxGXmFp/avpmB7zOqaeiAZjJZDIkrfZxj2JCSV1rQpVXW0AAqsuXMy287MpX58s31b5h4O6B/HHxD7qU6pLZIQlCtmW0Gll6YSmzTs/iQdwDyvqWZXbD2bQo1OKVnyC7qFx4r/h7vFvsXQ4GH2TWqVlMPDqRhWcX8mXFL+lSqosYX5ZDSJLE4eDDzD49m39u/4MMGbXz1GZw5cE0zt8YT43nSz+HTCajiFcRingVoXPJzlhtVg4/PMzfN/5mw/UNbLm1hUCXQN4v/j4flvqQ3G4vP52YIAgZY/bp2eR2zc3bRd5O1+2+1qmnKQkPDyc8PBwfHx/HtBRCxpFcXJI1FJP2KMpDQsBmA3kaesXSuvxr6P3i77PyykrGHh5LkwJNXjodThByGptkY8O1Dfxw9Adux9ymWlA1JteZTL289TJ9rJZMJqNGrhrUyFWDUyGnGHt4LCMPjGT+2fmMqDqCloVaZnqMQsaQJIl/bv/DtJPTOPHoBN4ab76s+CWdSnYij1ueDH1uhVzh+NyNrD6Sf2//yx8X/2DmqZnMPjWbdkXb0btcb0r4lMjQOARBSJvjj45z4MEBRlUfhUqezg26xNTTDJpiMKOlurUQGhrKN998Q58+fZgwYQJ9+vRh5MiRhISEZGR8OZ7k5oYsLg6wj1GUVCokDw/APkZRZrEgDw93LK+4dQufbt3QJFSMfZJm+3ZyFSyIx5gxGR98FiaTyfih9g8YrUZGHRiV2eEIQray995eWqxrQd+dfXFTubG0+VJWt1pN/Xz1s1wDrLx/eVa0WMHS5ktxVbnS699edN3SlZvRNzM7NCGdHXpwiLZ/teWjbR8Rrg9nbK2xHOl0hMGVB2d4I/FJKrmK5gWbs7j5YvZ32M+HpT/kr+t/0XB1Q7pv7c7ph6dfaTyCIDzd7FOz8dJ40blk53Tftsxor/CdXXsUU91QnDlzJoULF+bXX39lwYIF/PrrrxQpUoSZM2dmZHw5nuTqiiw2FgBFaCg2X19IOBGzJUyRIU9SdMh14UK027fjNXy4vefwCR4//IDMasV1wQKnBmZOVMSrCP0r9uev63/xz61/MjscQcjyHsQ94LN/P6PD5g5EGCKYVn8aW9tvzZINxKRkMhn189VnS7stfFvjW448PELDVQ2ZcnwKJmvyeRuF7OVa5DW6be1G+43tuRNzhx9q/8Cu93fxUcH3cb92O7PDI597PsbUHMORTkf4qtJXHA4+TJVfqtB3R19uRN3I7PAEIUe7FnmNv2/+zYelPsyQIlSJxWxe+4bi9evX6dKlC9qESpxarZYuXbpw/fr1DAtOAJubG/KEhqI8LMzeUExgTag2m3SKDPXRo/b7HjxAdfy407bk9+6hOn+e+PfeQ2axoP3774wOP8vrXa43JbxLMGL/COLMcZkdjiBkSWabmTmn51BvZT3+ufUPg94cxK73dvFOsXeyVTEopVzJJ2U/4b/3/qNpgaZMPjaZ1utbcyH8QmaHJrwAvUXPxKMTaby6MYceHGJYlWHs67DPPhZVb8KveXMCGjbE5ddfX11QVite/fsTUKsWips3nR7y0fow8M2B7P9gP1/X+Jqtt7ZSb2U9huwZQpjeea5q+YMH6FavhoRidoIgZIy5Z+aiVqjpUaZHxjxBwhjF1z71tFixYly9etXpvmvXrlG8uJheICM9mXpqTRifCGB7oqEo0+tRnTtHXMeOwONGYyLNvn0AxPbsiTUgAPXBgxkef1anVqiZWGci92PvM/bw2MwORxCynMPBh2m2phljDo2helB1dry7gwGVBqBVajM7tBeWyzUXcxrNYWGThQTHB9NibQtmnpyJ1WbN7NCEVPr39r80XNWQqSem0qpwK3a/v5vPK3zumA7FZcUKVNeuYQ0IwGPcOGQxMa8kLu3ff+OyahXKmzdxnzIlxWW8NF6MqT/GnpJa6kOWX1pO7T9rM//MfMw2M0gSPh9/jHe/fniMH/9K4s4qVEeOoDp2LLPDEHKIR/GPWHVlFe8Vey/Nc/qmVnYvZpPqhmJgYCDjx49n6tSpLFmyhKlTpzJ+/HiCgoJYsWKF4yakL8nFxZF6Kk9MPU1gfSL1VHXqFDKLBUOzZljy50f9xMFWdeYMNhcXLCVLYqpWDc2BAyBJ6ResxZK+23tFKgdW5pOyn/Db+d/YfW938gUkCcXdu44GuyDkBHHmOP6373+0+6sdceY4FjVdxOLmiyngUSCzQ0s3zQs2Z8c7O2hcoDHjjoyj3V/tuB2d+amKwtNFGaPo/19/Ptz6IRqFhj9b/sn0BtMJcAlwWk63fj3mMmUIX7QIeVwcLitXvpL4XJYvx5I3L3EdO9qzdkxPT20OcAng+1rfs/2d7VQKqMTog6NpsroJ+/6dh/rUKfv2/vgjx/z2qA8fxq9dO/zbtEH1xIVuQcgIv5z7BZPVRK9yvTLsORypp9l0eoxUNxTNZjPVqlVDpVIRHR2NSqWiatWqmEwmwsLCHDchfdnc3JAn9iiGhjoqngKg0WD19nb0KCb2IJrffBNz+fKozp512pbq3DkspUuDXI6xenUUDx6guJ0+J0XqvXvJVawYXs+Ye1MeEoJ2/XpHFdesZEiVIRTxLMJXu74i2hT9+AGzGe+ePQmsVo3ASpXQbt363G1p/vuPgDp18OnSBVlERAZGLQgZY8+9PTRa1Yhfz/9K76iS7Kq5lKYFmqa8sMmEy6+/Jkt1TzeShOrECVSnTqV8IcpmQ/vXX6j37HmhzfvqfJnXaB4zGszgSuQVmq1txqYbm14yaCEj7Lyzk4arG7L26lq+rPgl29pvo1buWsmWk0VGojp+HEOTJpgrVMBUoQIuS5dm/IVMgwHNgQMYmjXD2Lgx8ri4ZBdsU1LMuxhLmi9hUdNFmG1m3r/xHe90gLNzJyGPj0e9f3/Gxp1FuE2fjuTujs3FBffp0zM7HOF1ZLWiuHEDJIlYUyyLzy/mrUJvUdizcMY9Z+LFomzaUEz19Bh9+vTJyDiEp0gsZiOLj0eu1zv1KALYgoKQBwcDoDp2DEvhwth8fDCXLo3ur7+QRUUheXqCzYbq3Dn077wDgKlqVQDUR46gL5CKHgK9Hu8BA5DkciKnToUnutA9xo5FZjLhsmYNsf36YSlWzHl9iwWfLl1Qnz2LvnlzIhYscBTlyQp0Sh1T60+lzYY2fHvgW36s9yMA7hMnotu8mZg+fdDs3493796Erl2LuXz5FLcjf/gQ7169sHl6otmzB8+RI4mcMSP9A5YkdOvWgdVq36dZ6L18aZIERiNos29qY3YVY4phzKExLL24lMK6POz6VUadmxcxHhhC2KpVKX7O3H/6Cffp07G5uPBo/35s/umYvmO14jVoEC5//glAXLduRI0d6xSHxzff4LZoEQDhs2ZheDvtc2DJZDLaFW3HmwFv0mdHH3pu70m30t34pto32TrF9nURZ47j24PfsvTiUop7FeeXt3+hvH/Kx2AA9enTyGw2jNWrAxDfoQNew4ahOnMGc7lyGRan+vBhZAYDxrp1Mb35pv2+48cx1ajx3HVlMhlNCzSlXt56LPm6DuOK32db6LeMr6mk6+7/MDZpkmFxZwWyyEg0u3YR27s3MrMZ10WLkMXEILm7Z3ZowmvE47vvcFuwgOjBg5nXyIUoUxR9ymVs+ybHpJ4CGI1Gbt26xaVLl5xuQsZJHKOYOIdi0jGKAJb8+VHeugWShProUUyVKwNgLlMGANUFe5EGxZ07yGNjHfdbSpTA5u6O+siRVMXhNmcOur/+wmX9evuV2STkDx+iPn2a2E8/RZLL0f71V7L1NTt3oj571t6A3bIlWVqJdtMmvAYORHn5cqriyQgVAyrSt3xfll9ezj+3/kGzbRvus2YR17UrMSNGEPb779i8vfH68sunFhhIbDCHLV9ObO/e6NatQ/HE2N70oN20Ce/PP8e7f/9k+yOrcps5k8CKFdElnPSnRKbX49e2LbmKF8d1zpxXGF0WZLPhNnMm7j/88Mz0tfSy885OGq5qyLJLy/is3GccPlaNWqEuxAwciObgQVSnUyjnb7Xi+vvvmEuXRmYw4Prbb+kak/vkybj8+ScxffsS+/HHuP72G66//OJ4XL17N26LFhHXqRPmMmXwmDAhxWrPqZXfIz9rWq+h1xu9+O38b7Re35prkdfS46UIL+hS+CVarGvBskvL6FOuD3+3+/uZjUSwD7MAMJctC4D+7beRNBpcMnh4jHb3biSVClONGkje3ljy5Uv5e/MMGpmKEX9Fcii4PZUCKvFFUwtNdcs4H3beaTnVyZPP72mUJGRRUWl9GZlCffgwMqsVY/36GJo3R2YyodmxI7PDEl4jsshIx2+U6pd5zD09lxq5alAxoGLGPm/i7/fr3lDctWsXPXv25LvvvuPnn392ugkZR3JzQyZJKO7cAUjWo2gtWBDlrVsorl9HER7uuIrpaCiet/+4JKahJt6PQoGpcuXUNRQlCdelSzHUr29P4Vm+3OlhzW77uL74d9/FXLo0mhSK5Og2bsTm5UXoypXY3N1xTegBAFAfPIhPz564rFiBb8eO6f7DJr9/39Hr+jQyvR6XJUsYfi0fpT1L8PXOgViHfI6pbFmiRo8GQPLxIXLSJFSXL+P+00/JtqE+fBiX1auJ7dULa+HCxPXoAXJ5hoyNcZ82DXPx4pgqV7bH8goaEi9DefEi7hMmoHj0CM+RI586NYvr/Pmojx7FUrw4nmPGvL5FDSQJ3YoVeH79NaqTJ1NcxGXpUjzGjcN92jRcFy7MsFCijFEM3DWQLlu64KpyZX2b9XxTdiDeG7egb92a2E8+QVKr7T3YT1CdOoU8MpKYzz/HVK0a2s2b0y0u7YYNuE+bRlzHjsQMG0b06NHomzbFY8wY+3jsyEi8Bg3CXKQIUd99R2yvXihv337pFFi1Qs031b/ht2a/8SDuAW+teyvLpqLKQ0PRbtr02lbG/PPyn7RY14IoYxTLWyxnRLURqerhVZ09iyVfPiRvbwAkT0/0zZvbP8MJc5plBM2uXZgqV0ZytZfYN7/xhqPRmlqKGzeQx8WRv0QN/njrD+bZ2nLDxUjztc0Ze2gseose1alT+LVpg99776HZuTPlDUkSXn37kqt0adzTWhAnE+oNaA4dQlKrMVWsiOnNN7H6+KDdvv2VxiBkLnloKH4tWuDdowdY07+wmObgQWRmMzFffsmSAlEExwfzRYUv0v15kjGb7eMTs2nmV6obikuWLOGrr75i4cKFzJ492+kmZBybpycAyoReKduTPYqFCiEzGNBt3Ajg6FG0BQZi9fFBee4cYP/hlBQKzCVKONY1Va6M6tIlZJGRz4xBee4cigcP0Ldpg751a9RnzjiNbdTs3o3Vzw9L6dKYqlWzn9w/0XBRHziAsU4dJC8v4j/4AN2mTcjv3QOzGc9hw7DkzUvoqlUogoOdegxSS7Nrl71wwBM/bpodOwisVYvAWrWe3ii2WPDp2hWvIUMI+uprlk6+RqQ+nB4tzIQvXOiUAmls2JC4zp1xmzUL9e4khW/MZjyHD8eaKxexCeM0bQEBGOvVw2XVqpfq5XiS4upVVOfOEd+lCzF9+6J4+NDRWM+qXJYtA6WS0JUrkcfGot2wIflCVisuS5dirF2b0PXrsQYG4jlqVLq+d1mF65w5eA8ciMsff+D77rtw8aLzAlYrbjNmYKxSBUPdurgtWJAh78N/d/6j0epGrLqyii8qfMHW9lupFFAJ7aZNyOPj0b//PpKnp734VQqfMc2uXUgyGaY6dTA0bozq0iXkjx69XFCShHbzZrwHDMBYpcrjVFO5nMiffsLq749P9+74fvABiocPiZw2DXQ6DE2bImk06FL6bKWSPDwczb//gsFA4/yN2dZ+G8W9i9Nze0/GHR6HxWZJcT1ZVBQeo0YRUKUKPl27In/w4OlPYjKhuHv3hWNM+pz+zZrh07Mnvl26ZMiJVZrYbHgOH05AlSrPvLChOnbsuQVa9BY9X+36igG7BlAxoOJTxyI+9TnOnHH0Jjq22aED8shIdKm5mCFJuP/wA/4NGuCyeHGqnlMeEoLq/HmM9eo57jOXK4fy1q3n/sY6xZ7wm20uWxaZTEbbGr24MAM+UFdl1ulZNFzVkEO/jEByc8Pq44Pb1Kkpbkezcycu69djyZ8f9xkzUB84kKrnd12wgFxFiuD7zjtPrRSrOnLEfoEiHRuT6oMHMVWsaP+9VSgw1quHZtcu5+Oe2Yz7xIl4/O9/WeviiMWC92ef4V+/PsorVzI7msdMptTtoyxShNB17lzUp06h27o1Q3qT1QcOYNNqiezzGRPqynkz3pu6eeqmfv09e17o4rXMZMq2hWwgDQ1FpVJJ6dKlMyyQkydP0r9/f7744gvWpXDlWpIkfvnlF7744gsGDRrkNH/j89bNzhJ7EFUJKZlP9ihaSpYEwG3uXGyenlgSpyuRybCUKfO4R/HMGftjOp1jXcc4xedUF9MmXLE0NmiAoUUL+31btiQEaEOzaxfGunVBLsdUvTpyg8Ep3UZ+7x7Ke/cczxf3ySf2mGfPxm3OHFSXLxM1ZgymGjUwNGmC24IFyOLjH6//6BG65cuR37+fYnwuS5bg26kTPp984nTlVKbX4zlkCNY8ebB5e+MxcmSKB0Td6tVoDhwgcsIEQjZtosjbPfleX4dNBU3MjUg+12T06NFYihbF+4svHGml7pMno7pwgajvv0dycXEsq3/nHRTBwagPH37me5wWmr17ATA0boyxfn1sXl7o1q9Pt+2n+4+G1Yrur78wNGyIqWZNzCVKOC5sJKU+cgTl3bvEdeqE5OpK9PDhqE+ceGaqqoPFgtv06fg3box/kyZ4jBqV6pRfmV6P28yZzg3/J0kSimvX0qX6oPLyZTzGj0ffqhUPjxwBtRrlgAFO77tm1y77e9Gjh+Mz9LSexxcRa4rl6z1f03lLZ9xUbmx4ewNDqwxFo7DP8+SyciWWggUxVakCgLFOHVQXLyZrBGp27cJcrhw2Hx9M1aoBoD506IVikoeF4fbTT/g3aoTPp59iLlGCiIULneaekry9Cf/9d2x+figePiRi2jTMFSrYH3N3x1irlv3k8gWf379pU3w//BDfTp3AYiG3W25Wt1pNl5JdmHlqJl22dCHckNAbLkmoTp7EY+RIAmvWxHXhQixlyqA+dAjfDz+EhEp3Ts8REkJAgwYEVquG21OmTkgttwULkD98SFznzmgOHHCM48wsLr//bk/rUqnw/OabFBsm6t278WvbFq/Bg/H5+OMUjzVXI6/Sen1rVlxeQf+K/VnRYkWyiqYYjU9txMhiYlDeuJGsoWisXRtziRK4T5r03EaGbtUq3KdNQx4VhdewYc8+NiRI7Nkz1q/vuO/JISCpoTp3DkmpxJzwW24uUwZvtSdzzxRiVatVqGwyWpc9QafeQdz6uCOaI0dSvDDh8scfWP39Cdm2DWtgIO5PaVAmpbh+HY/vv8dStCjqw4ft79WT8Z08iX/btvj07InbzJmpfl3PIouNRXXmjOMYAvbzDUVYmFNRPvdp03CfOhW3RYvwmDgxXZ47Pbj88Qe6v/5CdeUKXl99lSUaXm4zZ5KraNFnNvjloaH4tm9ProIF8erXz1FhP73IHz5Ece1aqhurLqtXY2jYEKufX4oZLC9LdfEillKlWP/gH6572Rix3YTMknDxz2ZDu2ULiqfMDa+4cwe/Dz7Av00bZE/JiHoamcmUbccnQhoaih06dGDx4sVER0c/f+E0stlsLFy4kOHDhzNlyhT27dvH3SeuuJ44cYLg4GCmTZtGz549WbBgQarXzc6sCQ3DxJ7BxCkxEpnKlUNSqZBHRWGoXx/kj3epuXRpVJcugdmM6vRpzG+84bxuxYrYdDq0//77zBg0//2HuUwZbAEBWPPnx1yypCMlRHn+PIqwMHtDEZwG8CdKbIgmnnRa8+YlvkMH+8F+wgT0LVpgbGqvqBjTty/yyEhHeqv8wQP8mzfH+6uv8G/RwjFW08FgwH3CBIzVqhH/7ru4zZrluOLjNmMGyvv3ifzxR2L690d95kyK40VcFy/GXKoU8V26YK5QgZgRI+g0YBnNCjRj7OGxnAo55bS85OJCxPz5IEn4t2iB77vv4j5jBnGdOmFo3tw5vCZNsGm1aWvISdIzD6yaQ4ew5sqFNX9+UKvRt2xpr8aaDtVkNbt3E1SyJN6ffppuP3bqgwdRPHyIPqHIiLFhQ9RHjzpdDAD7iZakUGBs0ACwN7KNVaviMXbsUy8SAChu3sSvXTs8JkzA5uWF1d8f199/J6B+fby+/PK586d5DRiAx7hx+Hbu/NQGvceoUQTWrWufRPslx5y6T5iA5OJC1Pjx2HLlImbgQOQ7dqBJUrXTZckSrL6+GJo3x9iwIfB4HtQ0MxpR791r/8EG9t7bS6PVjVh2aRl9C3bmnzfnUcG/gmNxxc2baPbvJ/7ddx2pMonf78SLFACy6GjUx487HjOXLYtNp3uhiyLKixfxr18f959+wubpSeSkSYSuXZvswhjYL46F/PMPD0+cSFa4xlizJqqrVx1TBjmvaMF1zhz72NcUUrVd58yxN7y6dEFz6JCjZ1Kj0PBDnR/4se6PHA4+TPO1zbmwcyn+TZrg37IlrkuXYqxVi5AtWwj/9Vcip01Ddf58ipkRHuPGoXjwAGO1avaU8TQ0IJxIEro//8RYty5RP/yAqVw53GbMsKcMvsC2NP/8g+eIEXgOG2a/qn/4cNqqU0sSrgsXYqpQgZB//8WSJ0/ybACrFc9Ro7AWLEj04MFo9uxB888/TptZf209Lda14FH0PZaVHc/Xlb9GIVc4LSMPDiagdm0CK1RI8bOWeHH0yd87FAqiRo9GeesW3p9//vR0/YSxweYyZXi4bx+WQoXwGjbsuSmr2u3bsQYFOTVQzQkX1xNjSg3VuXP2YnCJF0gUCozVqqHZv58auWqwP+w9Ru6CNbprVPH8ncXlQf3kscFoRLNrF4a33kJydyf200/R7Nnz3ItNHt9/j6RWE7ZsGfEffIDr0qXJhgm4T5mCzcsLY9WquE2blqbe0qdRHzuGzGrFlFB8CHD0zCb2LMmionCdNw99y5bEv/MOLr//nua5MeXh4Xh9+SV+zZrh/sMPqRrmot6zB+9PP8Xl11+f+pvoungxpvLliRw7FvWxY6hOnEhTXOlNdfo07uPHYy5TBvWRI/Zx7inwGjQI9alT6Nu1Q7d2LX6tWjl+J16W++TJBL75JoF16+LfqJH9u/6McwrlpUv284RWrewX/A4eTPcGt/LSJUzFizH9xHRKqfLw9ok4x3zirvPm4fPxx/i1bZvs3ASwZ5ok/j+tc5CbzU4XPLObVDcUc+fOzdGjR/n000/p0KGD0+1lXb16laCgIAIDA1EqldSsWZMjT6QJHj16lLp16yKTyShevDhxcXFERESkat3szNGjeOaMvdH4ZCVIjQZ9Qi9fYkXTROYyZZAZjWj27kURFobpyWpvOh3Gxo3RbdiALCYG1bFjaDdudLoSLouNRX30KIaEk3ew92SpDx1CFh2N9r//gMcnkrbAQCx58zqVBNccPozN1RVzqVKO+6K//ZbYjz4i9qOPiEwy3s9cpQrGKlVwnTMHWVQUPj17IouJIXLyZOSRkcnGBuq2bEEREUHsl18SNXYs1ly58O7fH9WxY7jNmkV827aYqlVD37o1kkqFbu1ap/UV16+jPnnS6aQY7BXofqz7I/46f/rs6EOMyfkHyVKsGKEbN2Jo0sQ+RmvAAKImTOBJkqsrxiZN7Gk6qTiJk4eE4N+wIUGlS6eceiFJqA8dslfzS4hX37o18rg4tC+bqmE24zVwILK4OHSbN9s/C+lAt349NhcXR9U+Y506yMzmZCd5jvE9Hh72O2Qyon74AZnZjN/bb+M+bhzuEyfi/sMPuC5YgPbvv3GbNg3/pk1RXr1K+KxZhK1aRfgff/Dw8GHiPvsM3Zo1+LVp89STGdXp0+j++ovYzz7DFhCQ4g+q+vBh3BYutH/PrFZ8evZ07pGQJBTXr6eqt1F95Ai6rVuJ7dMHm48PAHFduyLlz4/7xIn2bd27h3b7duI/+ADUansV42LFXqgBJg8Px79FC/w6dMCtUV2+G1ODDps7oDbb+PdkJWZ0X0qBOg2cCjS5/PEHklxOfJJju7lMGaze3k7pp5p9+xzFJ+xvpgrzm2+m+UdUcfcuvp07g0pFyPbthK1dS3ynTi/0w2qqWdMeWwq9We6TJuE5ZgyeY8bg8d13To/J4uJwXbIEQ8uWRI0fj7lYMdxmz3Y6UfmgxAesbb0WjEZaXvyaxXkeETlxIsEnThAxbx6WhAaCoVkzDA0a4D5tmtOJqOLmTXSrVxPXtau96rNKheIFCzYpr15FefcuhpYtQSYjtn9/lDdvpv0qvMGA9yef4Nu9O7qVK9GtX4/nd9/h164dQSVL4vfWW3iOGIHyOUXr1Hv2oLp2jbiPPkLS6Yj5+mtU5845TSek3bgR1eXLRA8aROznn2MpWNDeyyVJGK1Ghu8bTp8dfXjD6M3JidG8/96wFFPUPcaNQxEejuTqiuewYclOJh3j8Z/oUQQw1a1L1Lffovv7b3w7dUrxuKDZsQPVlSvE9u4NOh1R33+P8ubNZw6JkOn19oZZo0ZOvyOOISBp6VE8ezZZ7KZatVDevIni2jV8/viTEYYabG2/jcI+xejWDt69O56b0Tcdy6sPH0YeH+/43Y7v2hWblxfuz+jFVu/daz829euHLSCAuE8/RWYwOBVLU50+jXb7dmJ79iT622+Rx8WlmB2SVuoDB5AS6iYksvn5YSpfHk3COYbr778jj40lpl8/4rt0QR4fn7b0RKsVn27d0G3YgOTqitv06QTUqWN/fU9J29bs2oVv165ot2/Ha8SIZOcPAIpbt1BduIC+fXv07dvb099TGs994gTa9evTliIuSfbhCe+/j/uECamuReD288/YvL0JW76c+I4d7Q3+Jy6eKc+eRfvPP8R88QWRP/9M2LJlyEND8W/Z0p7F8xKNNM2OHbhPmYL+7beJHDcOmcmEb/fuePfq9dTGuSahMJOpVi1MVauiCA5Gce9eisuqDxywN9zTcGFMHh6OIiSEtSUkLkde5vOqg0CjRbdlC7LYWNxmzMCm1aIIC7MPY3ryOY8dw+blhaRWpzjljeLaNTzGjEnx9cmMxpzRozh9+nTq1q3LpEmTmDp1qtPtZYWHh+Ob5Mqxr68v4U9cxQoPD8cvyfi8xGVSs252ljgmUW4wYM2dO8VlIn/8kYd79mBs1MjpflPCFA7uCSkaiT16ScV+9hnyyEiCypfHv00bfHr1wvuzzxwHCfX+/cgsFkdDEOwNRZnFgmbnTrTbtmEqWxZbYODj533zTacvkvrIEcyVKoHy8Wwskk5H9PffE/3998nKX8d8/TXKe/cIrFwZ9fHjRE6ZQnzHjsS/8w66FSuc5iZ0WboUS/78GGvXRnJzI3L6dBS3buHfpg02Nzeiv/3W/nxeXhgaNED3119OV7l169YhyWSO3q6kvLXezGo4izsxdxi4ayDSEwdOa/78RM6cScj27cQMGgQKRbJtgL3iniIszHEgfCpJsld+vXkTm5ubPRXkicaH4uZNFA8fOqXomGrUsKdqPK/XUpJwmzkT/8aNcUmhOqV2+3YUDx4Q/ssvWPLkSZ8KgSYTuk2bMDRrhpSQ9myqWhVJrXbqQZOHhqI+c8ZpfA+ApXhxwv74A1tgIG5z5+I2bRpuM2bgOWoUPp98gscPP2CqVo2Q7dudepdsfn5E/+9/hC1ZgvLmTbz790/xh89l2TIkrZaY/v2J69EDzcGDya6oui5YgNXbm4hp04icPh3VpUt4JKRkyWJj8fr8cwLr1CGgVi2UT441TEqScB87FmtgoCP9GrDPh5qQZqv55x/H+K74bt0ev41Vq9q/U2kZpyhJeA0YgPL6df7+6UveGObFvMDbfHkAznxzn7o7rxI9aBAxffvisnIlvl26oLx4EdfffsPQvDm2pMcbuRxTrVr2fZbwPmp27cLm6oqpUiXHYsbq1VFeuJDij6Xy6lU8hwzB/ccfHY/Lw8Lw6dQJmV5P2NKljlT6F2UuWxabh0eyapCy8HDc5s8nvn17Yj/6CNfffnMaS+SyYgXy6Ghie/YEuZy4Tz9Fdf58srT88j5lObzSm5oPNXxSO4x+Bc5gcHvi4p1MRvTQocgjI3FL0hB0nzYNlErHRQJ9ixbIly1LnglgseA5ZAh+zZs/tRBK4nfHWKcOAIamTTGXLm1vCCS50Kc6cgTXOXNSnrzcbMa7Tx90W7YQNXIkwefOEXz+PMEnTxK2aBGxvXsjubuj+/NP/Js2RbdmTcpvOuC6aBFWX1/0rVsDoG/bFkuhQvYLezYbWCy4//QT5hIlMLRqZX8fPv0U9cmT3DuwmbYb2vLb+d/oU7AzuyY8wK9qY8zlyuH5v/859RrJg4PRbdhAXOfORA8fjurixWSvTXXmDNaAAKffpKTiPvmEiGnTUB89il/btvax8km4zZmDJXdu9K1a2d/j+vUxNG6M+88/P069liT7sTnhu6BbswZ5bGyyi7XIZFhKlUp1j6L80SMUISGPi84l0LdogSST4dupE8rbt4n78ENK+JRgbZt1TL1WkqOqhzRa1YiZJ2ditpnR7tplr75au7Y9XDc3Yj/7DO327aiTZAU4mM14jh6NJV8+YhOOTZYSJTDWqYPrr786GihuCb2JcT16YH7jDcxFi6bYeEorzf79mMuVcxQBSmRs0AD1sWMor17Fde5cDPXrYylb1l7sxs8vVfMaJ3KdN89+PvHTT4StWUPIli1YihbF6+uv8W/WDLepU+3ft4RjrOrMGbw/+QRLsWIEHz+OqXx5+7nUE8fgxIwsQ6NGSB4eGBo1sp9nJGkQqvfvx691a3z69MFz+PDUBWyz4TFqFF6DB6O4exf36dPx/Oab564mf/jQcaFR8vS0X/CwWJJd6HCfOhWbh4e96B5gql2b0L//xlyqFN4DBuDTvfsLjTeXRUXhNXgw5hIliPzpJ+K7dePRjh1EDxuGdutW+3jqFLIV1Pv2YSlQAGvevI4LJcoUvjeKa9fw7dQJrxEjcJs2zekx5fnzeH/8cbJMBbD3JkrAZN0RCnoUpHXJdzDWr492yxZcf/kFRUQEYX/+iTUw0Kn3MJHq1CmM1aphLlXKMY44KY8JE3CbM8dej+HJ9ySbj1FESqXu3btLNpsttYunyf79+6XZs2c7/t61a5e0cOFCp2XGjRsnXbhwwfH3t99+K127di1V6yb6559/pCFDhkhDhgyRJEmSjEZjlrtZrVbn+wwGyaZSSRJI1lat0rY9g0GyFS8uSSDZ8uSRjAZDisuZ/vhDsnzwgWSeNUuyDB0qSSCZf/9dMhqNkqVXL8nm4iIZo6MfrxMfL9n8/SVb0aL2Zb/91ml75h9/lCSQjNeuScaHDyWbXC6ZR45MU+zm+fMla716knn+/MdxHjtmf76xY+33nTlj//u775xfz7ZtkmXwYMl45ozzNn/5RZJAMu3Z8/j9KVZMstav/8xYJu6ZKDEa6dsd36bpNThuUVGSzd1dsnz44bP3/YwZ9tczZYpk2rXL/v+ff3Z+DXPm2N/bkyed7rd89plk0+kkY1jYU7dvWrHC/lnw87O/D3//7fz8TZtKtrx5JWNcnGQZOFCyqVSSMTz8+a8vIkIyjx0rWevXlyzdu9v3e+Jzrlxpf671652fq359yfrGG49f16JF9uX270/V59p4755kOnBAMl648PzP0g8/2Lf955/Oj0VHSzZvb8nSoYP97xs3JJtcLlm+/vrxMrduSTalUrIMGPD4vf70U8kmk0mWQYMkW9Gi9nX69JFsQUGStWxZyRgfn/L7v2aNfZ/OmJF83xsM9m25udm317Wr82tYsMD+Gk6cSL7t+HjJ0q+fZK1WTTIvWeL0PYxVIfUfW1eSjZZJhX4uJG0/vFIyT58umX/5RTKGhDxedvFix3HG5u6e7LtjNBol86xZTp89W6FCyY5Jpm3b7HGuXeu8fmio/Zih1Uo2mUyyBQRIlqFD7a9Zp5NMO3a82Hcrpe9Ry5aSrUgR59gTjkmmo0cl4927ks3NTbK8847j/bMVKiRZa9R4vE5YmP0727mz83bmz5ckkPSLf5UG/j1QYjRStXnVpOuh15PFYXnvPcnm6ioZb9+WjKdPSzaFQrL06fP4vdq82R7TH384P8eYMfb9oFLZX0dMTPLX2Lq1ZCtY0Pm9X7368fH4wQPJ8t57iUnskgSS5b33JOODB48/Mx07Oo43z3xP792TrPXrSzaFQjJt3Zr88UuX7N+HoUOdX0fi8XbFCsmccGwzLV/u9B6vqaiTPEeqJK8JXtKqs6skS+fO9uPYzZuSaf9+e9z/+9/j93ToUMkmk0nG8+clY0iIZFOpJMvAgc7vTdmykrVZs+d+Tkxbt0o2Dw/JWrGiZIyKst+3d6/9PZkwwXn5M2ckm0YjWcuXlyz9+km2woXt+yh/fsnSu7dk8/WVrFWrpvgba+nXT7JptcmOC9YbNyTjuXPOMa1da3+f/vkn+Xa6d7efB1SoIBljYx+/z+PHS3fdkd7+pZnEaKRys8pJBxuWlKy1aztvIzxcshUpItmCguz7MUk8lsGDUzxGmv76y/5+zJ8vmQ4dsv9/1KjHz/3NN/b9cfPmi39nw8Lsx9ikx93E2+XLkk2hkGwuLvb4du9+HPNHH0k2Dw+n9+Kpt1On7Pvv7bed95HBIJmXLJGslSo5vie2wEDJ2ratZHN3l2z580vGGzfsr3XJkpR/y5o0kWxFiz5+z5Ytsy+3aZPju2YtX16yFSggWbp1s+/D06efHW9cnGT58EP75/+LLySjXi9ZvvzSvt2dO5+5rnnsWPtxOslzWNu2lWxeXo/PD06eTPE7azQaJaNeL5knTZJsOp1kLVNGMkZGpvxcDx9K5h9+kMwLF0rGuLjH++XDD+3HihR+yxPPQSxffeX8WHy8ZPPykiwffWT/OzQ0xXNLo9EoWb76SrIplZK1Zk37b1Xi71h0tGQrUMC+Dz08JOOjR87vy88/S38XRWI00pzDc+z3LVzo2O/Wli3t23/nHclWoIDz88bG2j+jQ4ZIlg8+SP640f57KIFkbd8+2WPW1q0d5zvJzvGzyO1ZlM9vStrVr1+f3bt3U++JK/7pwdfXl7CwMMffYWFheCeUtU66TGiS8WmJy1gslueum6hx48Y0btzY8Xfok+PdsgA/P79kcQUEBaG8c4f4wECi0xizrk8fPIcOJfqLL4hP8j45qVfPfgOwWvHbtAnF4MGEVapEwJo1GOrWJSImBpJc2XXr0QOPH37A5uJCaNu22JLEpSpRAn8gdvt2JI0GX5uNyPLlMaUl9hYt7DeAxPWCgvCtVQvFrFmEdumCx8yZqJRKQlu3dnp+ypSx35KuC8iqVSNIqcSwbBkxhQujOnUK/ytXiOrZk/hnxNapUCcOFj3I6N2jKaAtQJMCaZ/42LNtW1yWLyfk88+x5cmT7HHl2bP4Dx6MoUEDwt97D2Qy/EuVQlqyhND33nMs57V9OzIfH0L9/Jxem7ppU/zmzCFu+XL0bdsmD8Bqxf9//8NcrBihf/+Nf/36SIMHE/r33yCXI793j8B//iG2f39iIiPRVK6Mr9lMzN9/OxVneJLi5k18undHeeUKprJlUR48iGLDBsIXL8ZcsSI+s2Yh9/cnpEIFp3jdatbEY9w4ws+exRYUhNdffyHz9SUkXz6n5Z4pf377v89b/oMP8J83D775htAaNRzjeLUbN+ITEUFkmzYYQ0NBrcanQQNUv/1GaN++oFTaq9taLIS2b4814XlkX3+N97VraCdPxpIvH5ErV2KqXh3tG2/g07s3cXPnon//fecYbDb8R4zAUrAgj1q1Shazn58fkbNm4TVoEDZ/fyKGDEFKsoyyWDECgNgdO9A/MU7ZbcYMPKZNwxoUhLJLF2L37cNUoQJH5n5Nr6+03DLvplvpboyoOgJXlSuP2iesaDI9jqNRI1Rr1qDdsQN9ixZYfHySxaioVIlAQL9+PYaoKAJv3CDqk0+cvzuFC5NLpcK4bRvRCcWrEmNUh4QQsmEDqFR4fPcdmgkTsOTNS+Qff2AqUSL1+/05XKtWxXPTJiKOHcNaoIB9LPGCBZjKlyc0Vy4A3D/5BPeffyZ8506Uly7hfeMGEcOHY0gSg2f79rgsX86joUORfHyQ6fUEjByJqUIFwhs25itZE4q7FWfgroFUXViVOY3mUCPX44nVFV98QcCaNVj79kUeFoak1RLSs+fjY1XZsuTKlQvL4sVEJBx/ZbGxBP74I4bGjYnv1AmfHj2IXbbMnmKayGIhaNcu4lu1Iirpe1atGl5t2+IyahTSuHFgtRI9aBDxnTrh8scfuP/8M+zZQ/TQoWi3bkX9999EDx1K7PvvP/e9l82di1+rVsg7diRk+3an3jr3qVNRyeWEtG/vfBxu1Aj/okVRfPyxPSulenXCateG0FDMNjPjDo9j3tt6Kt+HmV0XU/CBDvkffxDbuzcxKhUUKID3W2+hmTqVsI4dQaMhYO5cDE2aEOHpCSYTPjVroly3jtCvvrLHqdcTdOECsQ0bEvO8z1PZsmimTsX3o48w9+lD1MSJ+IwahcLLi5B27Zy+f/j4oJk7F69Bg5DPmYOxenVM776L+sgRNAsWYM2Xj7BJk7Cm8BurK1QIb4OByCNHsBQtCth7q/zatkVlNhO2dCmmhJ5h9z17UMrlhBYo4Pz8AKNGoa1dG2O1akhJeuxV5cqRJwZ+MbzN2sbv8b99w6lZ+xG9zBX58v5N3NRujmWV8+bh07UrqmbNkFQqrHnyICkUKK5dI65TJ6Jq1XL+LFSsiH+pUshGjwZPT6ze3oR88IEjNmXDhgR89x3xS5YQ/9FHz3y75Q8f4jZzJvLISOI+/dQxhlTz77/4WixEVqxoPw4n5eqK64gReIwfT0z//sQUKeKIT1uzJj6LFhG9davT2MZkzGb8ElKiH40eje3JfdSgATRogDw0FM1//6HZuRPNwYOYqlQhasIErGq1/Tlr1SLQ1xfL3LlEJKTIyvR6gnbvJq5r18fnZlWrEuThgfmXX4isUAHdihV4nzpF+KxZmOrUIXDFCqSffyZ0zJiU4zWZ8P78c9SbNhH91VfEDhgA4eHIPv+cgMWLsY4ZQ/jvv6e8riQRsGABxqpVCfP1dbxXqu7d8V+3Dv2sWcT16IHXt9+i1GoJ6dzZ+TubqFMnNHny4NupE8aRI4kZOtTpYVlsLH5t2jhS0o2//krEzJmo9+7FZ/FiYr74gpgCBZIfV2rXxrNjR1x+/pnQ997Dmi+fPb5Tp/CPjCT6zTfRJ6wTkD8/5qNHiXhiG/4bNmCuXZuYwYPxb9nS/po++QSXJUvwunWL6KFD8ZgwgfiFC4n/8EPHep7HjzO2voLcroE0C2pGaGgosnr18KlRA3loKOHffIM1NBTXsmXxXL2a8AsXsPn7A/ZezECLheigIJRmM263bxN6755jeIQsNpZcN24AYL1wIdk5vE9sLHK5nNDQ0BTP8bOC3E/JWIQ0pJ5evXqVOXPm0L9/f0aNGuV0e1lFihThwYMHPHr0CIvFwv79+6mcJFcdoHLlyuzevRtJkrh8+TIuLi54e3unat3szpbQ8H0yHSU19O+9R/DVq8R37Zq6FRQKosaNQ/7wIf5Nm6IICXEaq5QotndvIseNI2zFimRTdpjLlEHSalEfP47mwAEkjcZe9jodxH38Mcp793CdPx+XZcswNG2K7YkT56eRvLww1q5tL48uSehWrULSaNAnPQlLgUwmY2KdiZT1K8sXO7/gamTai5nEfmGfq8dr0CC0W7bg8c03+LZti+8HH+D15Zf4duwIfn5ETpnyeOxhmzaojx59XEpfklAfOGD/UXxiPh5TlSpYcufG5Y8/Unx+3dq1qK5cIWbQIPsYokGDUJ8960jdcVmzBpkkEZ/QKHWkh6aUppRAcfMmfm3aoAgJIeyPPwjdupWQrVuR3Nzwfe89PEeMQLtzJ3Eff+yUdgzYCy+Bo/y55r//7Gmn8lQfklJPqSR2wABUFy86jbt0WbkSa1CQI30PIL5TJ/t0Izt2gMWCy++/Y6xdG2vhwo5lJBcXwhcvJvj4cR7t3es4STG0bo3pjTfs466eGDuh27AB1YULxAwe/NRJdy1lyhD699+EL17smP/N8VjhwvaUyifnCNTrcZs9G0Pjxjw8eJC4rl0x/Dab/lt60byTFZV/bta0WsO4WuNwVTmndT3JXKkSMYMGYXlKdWtrvnyYixVDu3mzY1ySIaEI1eMXqsNUoYKjQAAkjP+bMwdDgwaY33wTc7lyhK1axYPLl3l08KCjGnJ6MSQW/0moQqk8dw7VhQtOx7HYXr2w+vjg3bs3Ht9/j7lMmWSFqOK6dEFmNDrmQXWdNw9FcDDRI0c6vn+tC7dmU9tNeKg96LCpA/POzHOkqFuLFCG2Tx90f/2FZv9+okePdpx4AKBQYHvvPbQ7djjGyrksXWof89y/P4bGjbEGBqJbvdopLtWZM8ijozEmpBU6yGRE/vgj0SNGENe5MyGbNxM7YAC2wEBiBwwgdONGbO7ueH/5Jdp//yXqm28cx6XnkdzciJg/H1l8vD2NOyH9ThYbi+sff2Bo1iz5BTClkoiFCzGXLYuxenUiZs0CmYx7sfdo/1d75p2Zx8f53mXPL1D259/wHDECm5cXsX36ODYRM3Ag8pgY3BKO94qICOI++8zxuKFpU5TXrzsKTKnOnrUXRUnl742xaVNiPv8c16VL8enYEe2//xLbty+Sm1vyZZs04eGJEzy4fJnwZcuI7d+f8CVLeHDlCo9278aa0Ah8UmJBG2WSdDX3cePAzc0+BVCSatyqkyexFCuWLAUTALXaXpwmYWyzY/tly2Jzd0ezbx9vFXqL/X6j+OwozNGcpMGqBmy//XguQkvJkoTs3EnE9OnE9uqFqXx5LEWKEPn99ymOsUcmI2r8eBRhYSgvXiRq7NjHY8ixj9U3lyiBbtOz5xlV79mDf9OmuP7+O9rt2/Fr29aRHq79+29sbm4Yn3IciOvViwc3bhDz9ddO9xvr1EFSKp8+jyTYCyiNGGFPOR037pnnCjY/P/TvvkvkzJk8PHaM8N9/x5r0M61Wo2/bFu2//zpS59X79iEzGu1jUxNptejbtEG7aROKq1ftwyMqVcLQpg02Hx/i27VDvnx5ioV4ZHo9Pj16oNu0yf79HDjQcayRdDriPv0U7Y4dKJNUgk1KfegQyhs3iO/Y0el+c5UqmCpVsqff7t+Py9q1xH38sWOcfEqM9eoR364drvPnJ6uq65YwBCNsyRIiJ09GfegQATVr4v355xirVCFm4MCnbjdm4ECQy3GbNctxX+LY96THNHOpUsnG9sqiolBduYKpalXMFSpgqlzZPie3wYDbjBmYKla0j38uUADtE+mnB0KPsTevld7leqNW2NNAJZ2OsFWrCPnvP6x58zqeF3AaRqJMqIRqKVwYS8GCyCQJZZIp4pQJsxJYg4JSnLM7u6eepvqsrFGjRvTq1Yt27drRoEEDp9vLUigU9OjRg7FjxzJgwABq1KhBvnz52LZtG9u2bQOgYsWKBAQE0K9fP+bOncsnCXn0T1v3dRL1/ffoW7d2vqqcFmmc5NNcqRKxX3yB8t49DI0aOYqQOFGpiO/WzT728ElqtWMQunbbNvsA9SeL8LwgQ5MmmCpXxvP775HFxRGTcCU5tfRt2qC8eRPt1q24rFmDoUkTpIS5Kp9Fp9SxsMlC1Ao13bd2f1wiP5WsefIQNW4cmn378Pn4Y1yXLgW5HFlsLJpdu7CUKIF50yanE8nEcZOJ1RcVt2+jvHcPY0LBDidyOXEff4xm377kE46bzbj/9BOmsmUd05vo27V7PIbIYsFlxQqM1aphLVgQsB9ATRUrJhvr5WAw4NO9OzKrldD16x1jC61FixK6fj2WokVx/fVXjFWr2sd9PcFSujTWgAC0O3agOn4cRXi4o9ppRtC3aYO5aFHH65U/eoRm5057EaMkY0sNjRphDQjA9bff0G7ahPL+fccYDicymb1XJWkDWCYjdsAAe1GRpOO5zGbcJ03CXKoU+jZtXuwFyOX2BtgT1fS0O3far9B//DGSUskvPSpTcpg7K8orGPDG52x7bzvVclV7ykbTTv/++2gOHcJj0iSMtWs7j2NMYKpeHdWZM47xtS6LF6OIiCBmwACn5SRX1wyZgNia8GOeWNzJZdUqJLXa6b2XPDyInD7dPgbHYiHip5+SXaSwlC5tPxFZvBjl1au4TZuGvnnzZL0Xxb2Ls7ntZprkb8K3B7+l786+xJvtVfNihgwhdNUqQv7+216g5wm2Dz5AljCOF5MJt/nzMdaoYT+uKhTo27Wzn5gmKbqSePHGVCuFeQW1WmL79CF6zBgsT1xYNL/xBiFbthC6Zo39okKvXql/U7E3CqK//RbNnj24zp0LgOuvvyKPjHRq3DmtU7QoYatXE750KbbAQP69/S9N1zTlcsRl5jSaw3fNp2Lp9jEuq1ejOXyY6FGjnC6SWEqXRt+yJW5Tp+IxejTGGjWcLiwYEzKEEqtwJ1abTJwyJTViBg8m/p137JV+27dP8XjlIJcnv9CjUj3zc2wpVgxJoXCMU1ReuYJ2926s/fsTM2QIqitX7I0dSUJ16hTmhNoCqaZU2uc5TThW+x88yYx/NaxttgJ3tTvdtnaj1/Ze3Iu1j8WU3NzQt29PzLBhRM6aRcSiRfbewKeMsTdVqcLDAwd4tHt3sirDAIaWLVEfPJhsPJssMhLd8uX4dO2Kb8eO2Ly9Cdm6lUd79mApUACf7t3RbNuGbsMGDG+99exzhBTeX8ndHVOVKilWbVfcvYvH6NEEVq2K69KlxHz+eYqxp5W+fXtkRqNjLk7tjh3YXFycagaAvfaDzGIhoEED5CEhRI0Z43gN8V26IIuPTz6202bD+7PP0Pz3H5ETJ6b4/Yzr3h2bm5tTIyspl6VLsbm728cBPyF6yBAUd+/i9957WIOCiP388+e+3pivv0ZmtToVQVLcuWMf7/3OOxgbNCC+Y0dCNm7E0Lw5cZ98Qvhvv8EzGkW23LmJf/99XJYvdzSqNLt2YS5d2un8x1KyJMobN5wKx6lP2SvQJ14Iiv3kE5Q3b+L3/vso79yx/8bIZPaxrQcOOI3XHp/rMv4WDR1LOjein2RJmGs8cUo6eKKhmJDJpLhzx+k9ATBWq4YiIiJ5hWST6ZnvSVaX6oZi/fr1qVChAm5ubkiShM1mc9zSQ6VKlZg6dSrTp0+nfXt7blTTpk1pmnDFWiaT8cknnzB9+nR+/PFHihQp8sx1XyfmN98kYs6cFK9yZpSYIUMIPnGC8F9/faGTOX379qguXbJf3Xr33fQLTC4nbNEiYgYOJGzlyjQXv9C//TbWoCB8Pv74mSc4KcnjloeFTRbyIO4B3bZ2Q29J23QU8R078mjfPkLXrSP47FnC1qwhdONGHp44QdiqVZBwgEpkLVAAU6VKjkaHoypYSg1FIL5zZ6z+/niOGOFUHc118WKUt27Ze7MST4aVSmIGDEB1/jx+77yD8sYN4p5IHXKc8Kdw5dNt7lxUV64QMWOGI50qkS0ggNC//uLRjh3215VS9UqZDEOLFmi3bcNj8mRsLi7Je6fSk0Jhr8R45QquixfjNncu2GzEP5kiqlLZr9r+9x8+ffpgLlUKQ5J09ecxNG2KuUwZe+GShF5Ft3nzUN68SfSwYS/VY2quVAnlxYtOpbu1Gzdi9fHheEkv3t34Lv129aegb3G2vvMPg6oPc8yLmF7iunfHXKoUNhcX++tJgalGDWQWi/1qe3y8vcezXj3MCVPnvAqGhg3R7NuH/OFDdGvXYmjcOFkvrbF+fYJPn+bh8eOOiqVPivniC5Q3bxJQrx6oVEQ9US01kbvanflN5jO0ylA2XNtA6/WtuR51HWQyTDVqYH6y4nQCqVIlzCVL4jp/Pq6//YbiwQN78YkE+tatkVksTkU7NHv22PfBE5kcqaLVYqpW7amFXp4nvnNn9C1a4DFhAu7jx+M2ZQqGJk0wP6cHLzHV9MOtH5LbNTd/t/ub1oXthW+iR40ifMECQletQp8kzT5R5IQJGJo1w1ivHhFTpzr9Hlnz5sVcqpSjoag+fhxL7typzjIBQKkkcto0Hty6ReT06cmyH16aRoOlZElHNoDL0qVIKhW2jz5C36YN1qAg3ObMQXnunH2qqWelUT6FsVYtlDduIL93D82OHZiqVKFK/lpsabeFwW8OZvvt7dT9sy6Tj012XMRIC1tAANZChVJ8TN+qFTJJetzwkSRcfv2VwCpV8P7qK5RXrhDbty+hmzdjKV4cm68vYcuWYfP3x/ejj5CZTMT27ZvmmMA+1ZLqwgWnHi/l2bP4N2yI62+/YapQwX6+8JRjVVqZy5fHUriwvZffZkOzfbu9F+yJ3zhroUJETJ+OuXx5IpPM9Zq4DVu5crguWeJUYM39p5/Qbt9O1JgxxHfunOLzSx4exHftiu6vv1Ak6dECe8Eu3aZN6N95x2ku50Sm2rUJ//VXYnv2JHTVqmRFBFNizZ+fuA8/xGX58sdzRo8fj5RQsCuRpUwZIqdOtV/oScWF99i+fcFqxW3ePORhYaiPHHGqrA9gLlECmdWKMklxOdXx40gymeP9NLRsibFOHdTHjqFPMpWUsWZN5Hq9YzqYg5e3sSOfmQHyeuiUOp7F5u+PzcvLqdKz8vp1e8VTHx9HumzShqIyoSBWYseJ4smLJiYTUk6YHuPw4cP069ePP//8k3nz5rFlyxbmz5/PniSVC4XXiy0g4IVPbOPfeQd9s2boW7VKeczcS5B8fIj56qsXS1nTagmfPx9j9epETpqU5qu3VYKqMKPBDE48OkGfHX2w2NI2b5k1Xz5MVao4KoA+T3z79qguXEB58SLarVux5sqFJWEi5idJ7u5EjRuH+vRpvHv3RhYVhfLyZdwnTcJQv36yqrj6du3Qt2iB+uhR9G+9lazH2li9OjKbDfUT083IIiJwmz4d/VtvPb0XUKWyX5l7ylVqgNgePUAuR7NnD3Hdu2f4hRBDixYY6tfHc+RI3ObMIb5DB6xJLjg54vr0U+I+/BBjtWpEzJnzzNeQjExGzIABKG/cQLdqFcpz53D76Sf7j9gT739amSpWRGazPZ4LVK8nbs82en/oS/MNLbkUcYkJtSewrs06SviUePbGXpDk4kLIli08PH36qb02xho1sHl54bJ6Na6LFqEIC0vWm5jR4jt0QGYw4N+iBYrQUOKSVJB1otU+80qvsXFjImbMIL5DB8JWrkxxfHEiuUzOFxW+YOlbSwmOD6blupb8cyt59T0nMhnRCb1KnqNHY6xVy3GyAwknpvnyOVJ9ZbGxqA8ffua44QwlkxE5aRKmihVxnzEDa6FCRD5n4vN7sfd4d+O7zDw1k84lO7Ph7Q0U9nycyo1CgeGttzDVqJHi+pKPDxELFxL+++8pvv+Gxo3t00GEh6PZsyflntZMZqxZ0z5XYEwMuoRMFvz9Qa22z3G4bx+eo0cjyWRO+z/V209Yx3PMGFTXrjmGU6gVar6s9CW73ttFs4LNmHJ8CnVW1mH1ldXYpPS5yG8pUQJjjRq4zZmDPDwcz+HD8RoxAlP16oRs3syjAweIGTbMqfFiCwwk9M8/ienbl7Dly+3zRr6AxDTzxAsp8pAQfD76CMnDg0e7dxOxcKFjjuZ0IZPZe58PHMBt5kyU9+499fzG0KYNoRs3om/XLtk2bD16oDp3znEs12zbhvuUKcS/9x7x3bs/M4TYjz8GhQLXefOc7nf9/XdkRiNxXbo8dV1j48ZEjxr11EZ/is/Xrx+SRoPnqFFodu3CZf164nr1SjGbJLWsBQqgf/ttXBYvtlc2tliSVQxO7ARQJUkBVR8/jqVo0cfpz3I5Yb/9RshffxExd67jIlLisURz4ACSJDH56GRyxUDXQskvRCUjk2EuWdI+B3kC5fXrWBKGn9gCApDU6sdDggDFvXvYPD0dF82fTD/NMamnK1asoHfv3kycOBGtVsvEiRPp2bMnhdLwgRNyEJ2OiF9+sX95s9gXxFypEmGrV6eYCpYabxV6izE1x7Dt1jaG7xuebNqM9GRo0wZJpcJzxAg0//1nT597Rg+voUULor79Fu22bQRVqoR/kyZIGo19/MmT68nlRMybR/Dhw0TMm5fsooC5cmUkpdJpvBlgTxnR6585DiE1rEWKELZ8OVGjR9t7OzOaTEbErFnEfvwxsb16Ef399ykvp1IRNX48YWvWJOstTQ1Ds2aYKlfGa8gQ/N5+G8nbm6ixY18yeBy9NqoTJzBZTSz56xtKfqJnoddVupfuzt4Oe+laqityWQaM80xKqXz2hQ61mrjOndFt3IjHuHH2HqcUpubJSJayZYnr3h1FcDDxHTo4pgl4Efp27Yj86afkE7g/Rb289djSbgsFPArQfVt3Jh2dhNX29LnTjE2bEj5rFjH9+xO+YIHz91Qmw9CyJZo9e5BFRdnnrjSbHWN8M4Pk5UXY2rUEHz1KyNatz+y923ZrG03XNOVC+AVmNZzFxDoTn3tFP60MzZsjs1rx+vJL5JGRzuPFsghjw4bIjEa8+vVDERbmlMkQ37kzVj8/NAcOoH/7bedxrKlkKVoUY82a6P76C5uXV7LpnvK652VWw1msa72OQF0g/f7rR6t1rfjvzn/p8vsVPXQo8rAwgt54A9fFi4nt3Zvw336zX4h9yu+VLU8eYoYPf3YhmuewlCiBqVw5+zg1vd6eKRQeTviiRY6en/QW160bNi8vPCZMwFKoULKxzalh69gRm06H2/TpKM+dw7tfP0xvvEHk+PHPzeCy5cqFvn17+5jdhF4teXg4bnPnYmjcGEuS+arTg83Pj+gRI9D+9x++nTphLlr0hXuAk4oZOhQ0GnQbNqBv1cqR8pnIUrgwkkr1uGdPklCdOJE8e0GjSTb9ms3HB3OpUqgPHGDv/b0ciDvH8D2gKpG6Oh+W4sXt4w4TvhtJG4rI5Vhz50aZNPX03j2suXNjTcjyUISEOG1PZjRmufPgtEh1jkVoaCg1nrjiV69ePXr27MmHSSoLCUJO8FGZjwiOD2bGyRloFBq+q/EdsgwYb2Xz9SXu449xmzMHm1brmOPqWeI++QRj9eq4rFplL+TSo8fTr/7JZE/tJZFcXDCXL4/mwAEcyadWq33sYY0aTy16khamKlVSnN8zo0ienkQ/JX0w3cjlhC9ahPvkyfZxtAMGYAsKeunN2nx9MRYqwJqraxi9cjG3Y25TL1zJyI6bKBWQcupkZont1w/F3bvIo6KInDw5U2KI+v57Yj7/PF3e+7TK556Pta3XMnzfcH4+8TOHgw8zrcE0crnmSnF5w9tvY0jxEXv6qducOeg2b0a9bx82D490LwCUZjIZtlwpvxYAk9XE+CPjmXdmHmV8yzCn0RznXsR0ZK5QAWO1amj//RdrrlwYmjXLkOd5GcbatbEULIhu2zbMpUtjbNiQxMQ/yd2d0NWr0W3ZQtxTUg5TI2LqVNx/+sl+cfEp6X9Vgqqwse1GVl9ZzaRjk+i8pTNVA6vydZWvnSr2ppW5cmXClyxBmzBn7ov0ir4QmYy4Xr3w7tuXwGrVUISFET53bqov6rwIydub0BUrcFm+3D7X7YukFHp5Edu/Px4TJqD7+2+suXIRsXAhpDLTKOarr9Bu3Ih3375EzJiB5/DhyPR6p3TQ9BTfvTu2wECUly8T36VLysWW0siaJw8hW7ei3r8/xTGVqFRYihZ19Cgqbt9GER6e+kJVNWqgW/YHk4/EkcfiysfnLEQkFKx5HnOJErhGRyMPDkZydUURHOzU623Nly9Zj6I1Tx7H8Ab5k/MIZ/MexVQ3FD08PIiMjMTLywt/f38uX76Mu7t7uo1RFITsZmjloRgtRuafnY9cJmd09dEZ0liMHj4c8xtvYClSJNXpHpayZYl+yrirtEhMKZLFxyO5uKD95x+Ud+8SnYqJf3Mym48PUePGpd/2JBvbbm3jx47RnFffoqysOJuWK6lT60NislgjEewFMyKfUnDhlXlOYyaj6ZQ6fqr7E9WDqjNi/wiarG7CT/V+ommBtKXCmcuXx1yqFB7ffYcsJoa47t2z9NXpa5HX6PdfP06GnKRb6W58U+0btMr0KWb2NJHTp+O6YIE9DTArvjdyOeHz5+Py55/2NOgn0tmtRYumqrjIs9hy5yYqFRdl5DI57xV/jzZF2rDs0jKmn5jOuxvfpXbu2vSt0Jc6ueu80O+YsW5djHXrvkjoL0X/9tsoL1xAu2MH0aNGpdzoSGeWsmWfnpGSSrGff47N09NeH6BHD+cKq89hzZOHyClT8O7Th8CEDpzIiRPTvTcxKcNbb8Fbb6XrNq158yafSioJc4kSqI8eBXCM8TWlVEAxBaaaNdm36xeOhhxj+oWCKEr4pHoYiaOgzaVL2BLSXJM2FC358qFNKLQJoLh/H1OVKtgSLtDInmgoZvcxiqluKDZq1IiLFy9SvXp1WrZsybfffotMJqPVK/hSCkJWJJPJGFV9FDZsLDi7AEmSGF1jdPqn/ikU6T7OM7VM1asjmzED9dGjGOvWxfXXX7PsVfvXkclqYu3Vtcw+PZsrkVco7J6b5UsiaBcVh/quhUdTk09dI2QdMpmMDiU68Gbgm/Td0ZePtn3ER6U/4n/V/pf6xpNMRtTYsfh06YItKIjYL7/M0JhflCRJ/HbhN8YcHINWqWVuo7m0Kvxqzg+sefIQnQ5TdWUkS+nSRI8endlhOGgUGrqX7k6H4h34/cLvzDo1i46bO1LapzS9yvWiTeE2jmkEsjSZjJhhw9KtYM0rI5M5zfOXVoaWLQnZtAnN3r326SJS2YDKTiwlSuCybh2ymBjUR45gc3NLdQFDQ9WqjGwI+W0efPr3A0ydUl+ULnGKDNWZM1gTUuvNSXsU8+ZFERoKej0yqxV5ZKS9R9HVFUmhQJ6kSjXYG4pPmxYrO0h1Q7FtkhPVevXqUaZMGQwGA3lT2ZUrCK8jmUzGt9W/RY6c+WfnE2YIY0q9KdnjBzYVTFWrImm1aLdswZorF5o9e4gePPilKwNKkkS0KZpYcyzx5nj0Fj16ix4bNpQyJXKZHIVcgUahwUPtgZfGCxelS4b02GZFj+IfseLyCn499yvB8cGU9inNjAYzaF2oFYGbuqA+t5f4tm2fWq1TyFqKehVlw9sbGH94PPPPzudg8EGm1JtCA7/UTQljqlaNh0eP2tOXUpme9ioFxwXz1e6v+O/uf9TPW58f6/5IkOurT/kV0k6n1NHzjZ58WOpD1l1bx5zTc+j/X3/GHxlPxxId6VC8A/ncX68px14XlrJlX+vfgMQUYtXJk6gPH7b3Jqby3OOf2OMcyQML1kejiYe4p1SLT4nk5YWlYEFUJ08iK1wYSa3GmjAtBuAY/6q8d89R3dySN689i8XTM+emnj7J70VKcwvCayixZ9Ff58+4I+MIM4Qxv/F83NXPLz+d1UmuruhbtEC3ahWq06exuboS/4yqakmF6cO4HnWda1HXuB51nRtRN3gQ/4CQ+BBC9CEYrcbnbyQJhUyBh9qDQJdAcrvlJpdrLsetgEcBCnkUItAlMNs2Jq02K3vv72XJxSVsu7kNi2ShVu5a/Fj3R+rlred4XeG//orq2LHMH6cmpIlGoWF0jdHUzVuXQbsH0XJdSwbXGEzPkj1TNY1JasrOv2qSJPHn5T/57tB3GK1GxtUax4elPsy238GcTKvU8kGJD3i/+PvsvLOThWcX8vPxn/n5+M/UzlObD4p/QOP8jXFTv7ppuoSczVSlCpJCgW7jRpQXL6JPmAf6eaw2Kz8c/YFCVk8+PBWFzcsr2fQbz33uihXRHDiAPDoac4kSTg1Ua0IHmeLOHbBane6TPD1TTj3NiQ1FQRAek8lk9K3QFz8XPwbvHkyr9a1Y2GQhRb3SXjUzq4n56iu0W7eiPnGCyHHjUpy/LdwQzolHJzgdetp+CzlNcPzjEtEquYoCHgXI7ZqbwkGFCXQJxE/nh6fGE51Sh06pQ6vUIkeOTbJhlaxYJSsGi4FoUzTRpmiijFFEGiN5GP+QB3EPOBN6hhC9c3UxV5UrhTwKUdizsNOtiFcRPNQeGf5epZXVZuVg8EH+OfoPay+uJVQfirfGm4/Lfkynkp1S/PxIOt1LVfEUMlfDfA3Z8e4Ovjv4HRP2T2DN+TX8WO9HKgVkr9Sxq5FXGbp3KAceHKBqYFUm151MEa/k080I2YtcJqdR/kY0yt+IuzF3+fPyn6y4vIK+O/uiUWiom6cubxV6i0b5GuGnEx0GQsaR3NwwVa1qn3OShHGSqbD22lp7peW6UzDE3bHPdZnGhpq5QgVc1q5FERxMbM+eTo9ZkjYUE+q0JDYUbV5ezqmnNhsyiyVbj1GUSRlZ2z+Lu3//fmaHkIyfnx+hoaGZHYbwEvbd38dn/36GyWpiWv1pNCuYuvF8WXnfy+/dQx4R4UhzsdgsHH90nP/u/sd/d/7jdOhpJCRkyCjiVYRyfuUo61uWol5FKeJVhLxueVHK0/+6lNFqJDgumFvRt7gedd1xuxF9g9sxt53mCgvQBVDEq4i94ehZhCJeRSjqVZR8bvlQyNMwV+JLkCSJG9E32HtvL3vu72H//f1EGiNxUbnQMG9DWhVuRZP8TTK8+IeQNRyLOsZnmz7jQdwDOpXsxJDKQ/DV+WZ2WM8Ub45n1ulZzDw5E51Sx/+q/Y8PSnyQ8dOyvGay8vH+STbJxuHgw/x982/+vvk392LtE4yX8ilFzdw1qZmrJuX9yxPkEpQlepNNVhORxkgijZFEm6Kx2CyPb5IFpUyJVqlFq9SiUWjw0njhp/NDJX8148iy077PbJr//sOnSxeM9esTntBgfBaDxUDdlXXx1fqyqe2mFz4uyYODCXrzTQDCli1zLtRktZKrSBF7A1KScFuwgAfXroFcjk/nzsgjIwndtMm+rF5P7qJFiR42jNjPP8+y+z73MwolioZiFpNVP0RC2tyLvcen/3zKqdBTfFT6I0ZUG/Hc+cOy+r6/F3uPXXd3sfPuTvbe20u0KRq5TM6bAW9SL289queqTlnfslkm5dZkNXEr+hbXoq5xLfKa/d+oa1yNvEqkMdKxnFqupqBHQYp4FaGIZxEKexUmr1teAl0CCXQJxE3lluaTH0mSCNWHcjvmNrdibnEh7AJnws5wJvSM47lzu+ambp66NMjXgPcrvk98VHw6vnohO/Dz8+PG/Rv8dPwnfjn7Cy4qFwZWGkj3Mt1f2UlralltVlZdXcXEIxMJjg+mbZG2jK4+Gn+XtM/7J2T94/3TSJLEmdAz7L63m73393Ik+AgGq31yFz+dH2/4vkEJnxLkd89PAfcC5PfIT4AuAFeVa5qPo2ab2ZFJkvQWYYggwhjh+H+kMZIIY4Tj/7Hm2Bd6bT5aHwJdAingXoCi3kUp6lmUEt4lKOVbKl2/j9l132cWeWiovaJoKgrCzD09l+8OfceKFiuoneflMm90q1ahuH+f2C++SDa/ZUCtWpjLlQNAdfo0j/btA8C7d2+U584Rsns3YK+Amqt0aaJGjSKuZ88su+9FQ/EpRENRyEgGi4FxR8ax8OxCCnsWZkq9KVQOrPzU5bPavjdYDBx+eJj/7vzHf3f/41KEfeLbINcgGuRtQP289amdpzZeGq9MjfNFhBvCHzcekzQib0bdxCJZnJbVKXUEugTirnbHVemKi8oFV5UrSpkSGzasNis2yUaMOcZ+ImOIJNQQit6id2xDJVdR0qckb/i+QTn/ctTKXYtCHoUcJ05Zbd8Lr0bS/X418iqjDoziv7v/UdCjIP0r9qd90fYZ0hOfFpIk8c/tf5h0dBLnw89TMaAio6qNokrQq5v/9HX0unznjVYjp0NOcyb0jONi2PWo68nGoKvkKrw13nhpvNAoNSjlSlQyFUq5EovNgtFqxGQzYbAYMFjtQw7izHFPfV65TI6n2hMvjRfeWm+8Nd54a+3bT/p/D7UHKrn9eRRyBUqZEotkwWAxYLQa0Vv0RBojCYkP4ZH+EQ/jH3I96rrTb4FWoaW8f3kqB1amTp46VAuq9lIF616XfZ/VRBmjqLmiJhX8K7D0raUZ+ly+HTogi4sDmw2bpyfhy5YB4Pn112j/+YeHJ04AIA8JIahCBSLHjiW+e/csu++f1VAUYxQFIYNolVq+q/EdTfM3ZeDugby94W3eL/4+w6sMz5JX4RPTIhMbhvsf7Edv0aOWq6mWqxrvF3+fBnkbUNy7eJZIL3oZPloffIJ8kp3smm1m7sTc4X7sfR7pH/Eo/hHBccGE6EOIMcUQb4knTB/GrehbWCWrvTqrTIFcJsdN5Ya/zp9iXsXw0fqQ3z2/41bQs2CqCpYIOVdRr6Isab6Ef+/8y6SjkxiwawDTTkyjX8V+vF3k7Vf++bHYLGy+sZlpJ6dxIfwCBdwLMKvhLNoUbpPtv/9C+tEoNFQJquJ0LLVJNh7FP7JnVETfIlQf6tTjZ7QasdgsmG1mLDYLSrkSN7UbarkajVKDRqHBU+2Jp8YTb403nhp7gzDx78T/Z2S6s9lm5lb0Lc6FnePYo2Mcf3iceWfmMfPUTFxVrtTJXYfmBZvzVsG3RIGfLGLmqZlEGaMYXnV4hj+XJV8+tFu3Io+PJy7JNCeSuzuymBjH3zKTyf4fUcxGEISnqZ2nNjve2cHUE1OZf3Y+m29s5tM3PuXjMh/jrfXO1NjizHHsu7/PMdbwVswtAAp5FKJjiY7Uy1uPmrlq4qJyydQ4XxWVXOUogCMIr5pMJqNx/sY0yteIrbe28uOxHxmwawDfH/qeziU707VUV3K7Pf3Kb3p4GP+QPy7+wdKLS3kQ94CiXkWZWn8qbYu0zfTeTSF7kMvkBLkGEeQaRNWg7FmdWSVXUdSrKEW9ivJ2kbeBx7+XO+7s4N/b/7Ll1haG7h1K0wJNebfYuzTI10CM1c0k92LvsfDsQtoXbU8Z3zIZ/nyWEiVQJPQimkuXdtxvc3NDrteD2WxPlTXae9ZF1VNBEJ7JTe3GiGoj6FCiAxOOTGDK8SnMPzOfrqW60rlkZwp5FnolcVhtVk6Hnmb3vd3subeHow+PYraZcVG6UCt3LXqW60n9vPUp6FHwlcQjCEJyMpmM5gWb07RAU/be28ui84uYfnI6009Op3qu6rxd5G3eKvhWulWdDDeEs+XmFjbd2MTee3uxSBbq5qnLmBpjaFqg6Ssr9iQIWZmrypWmBZrStEBTJEni6KOjrL26lg3XNrDh+gYKuBegW+ludCjRIVsOycjOxh4eC8DXlb9+Jc9nrFfP8X9TpccVqyV3e40GWWwskrc3MrPZfn82biiKMYpZTFbNXxbS1/mw80w7OY3NNzZjlazUyl2LTuU6UdO3JgEuAen2PEar0Z468/AYh4IPsf/+fqJM9jl+yvqWpU6eOtTPW58qQVVEamQmEt/7nCkt+/129G1WXVnFumvruBZ1DbBXnayduzZvBr5JKZ9SFPIo9NxGndVm5Wb0TS5FXOLIwyMceHCAs6FnkZAo4F6A1oVb80GJD17ZxaucSnznXx9mm5m/b/zNonOLOPzwMDqljk4lO9GnXB+CXIOSLS/2ffo69OAQ7Te2Z2ClgXz15lev5kklCc+vv0bS6Yj+7jvH3boVK/AeOJCHBw5gzZ8f1Zkz+DdvTvgvv2Bo1izL7vssXcwmNjaWKVOmEBISgr+/PwMGDMDNzTnfOzQ0lJkzZxIZGWlPzWncmBYJE2/++eef/Pvvv3h42OdI69ixI5UqpW4+KtFQFDJbcFwwyy8tZ8XlFdyOuQ1AOb9yVA6sTKWAShT3Lk5Bj4K4qlyfuR2zzcyj+Edci7zGlcgrXIm8wrmwc5wNPYvJZs+Rz+uWlzp56lAnTx1q566d5cvw5yTie58zvch+lySJc+Hn2HF7B3vv7+Xow6OOwiEahYYglyD8Xfzx0fqglCmRyWSYbWbCDeGE6cO4H3ffaflKAZWokasGzQo0o4xvGTH+8BUR3/nX09mwsyw8u5DVV1ajlCvpWKIjfcv3dUoZF/s+/VhtVt5a9xYRhgh2v7/7udXlM5p282Z8Pv2UR9u2YSlTBtXRo/i//TZhS5ZgbNAgy+77LN1QXLJkCW5ubrRt25Z169YRGxtLly5dnJaJiIggIiKCwoULo9frGTp0KIMHDyZv3rz8+eefaLVa2rRpk+bnFg1FIauQJImH0kOWn1zO3vt7ORly0qlqpo/Wx1HBTafUYbaZMVvN6C16QvQhRBgjnLbnqfakpE9JKgVUctxSurIpZA3ie58zpcd+N1gMXI28yvnw81yKuMTDuIc80j8i3BCOTbIhSRIKuQIfrQ++Wl9yu+WmuHdxinsVp5RPKTFnZyYR3/nX263oW8w4OYM/L/+JQq7g0zc+5fPyn+Oudhf7Ph39fuF3hu4dyuyGs2lTJO3tgPSm3rMHvw8+IHT1akzVq6Pevx+/994j9M8/MdWqlWX3fZauenrkyBFGjx4NQL169Rg9enSyhqK3tzfe3vaiHzqdjjx58hAeHk7evHlfdbiCkCFkMhll/cvyZaUv+bLSl1hsFi5FXOJ61HVuRd/iTswdok3RxJhiMFgN6JQ6PNQeaBVaqueqToBLAP46fwp5FqKYVzH8df6iZ0AQcgCtUktZv7KU9Sub2aEIgpCggEcBJtWdRL+K/Zh4dCIzTs5g+aXlDH5zMJ/X+jyzw3stRBoj+eHID9TIVYPWhVtndjhAkjGKCZVPE6ueZucxipneUIyKinI0Ar29vYmOjn7m8o8ePeLGjRsULVrUcd/WrVvZvXs3hQsX5sMPP0yWuppakiRhMBiw2WyZdpL98OFDjEbj8xcUXjlJkpDL5Wi12gz/fCjlSsr4lnkl1bsEQRAEQUh/+dzzMb3BdD4u+zHfHfyOIXuHsOTyEsZWH8ubgW9mdnjZ2sSjE4kyRfFtjW+zzIVxW0JDUR4ba78jcXoMTfatAfFKGopjxowhMjIy2f0ffPBBmrZjMBj48ccf6d69Oy4u9nL9TZs25d133wVgxYoVLF68mD59+qS4/vbt29m+fTsAEyZMwM/PuWJcWFgYWq0WlUqVprjSmyYbf6Bed2azGblcjq9v+o/vUyqVyT6TQs4g9n3OJPZ7ziX2fc7S2K8xjUo2Yt2ldQz6dxBvb3ibnpV6MqbeGDy1npkdXrZz8O5BFp9fTN/KfalXot7zV3hVLBYA3G02XP38kCWcz3sFBCD5+WXL7/0raSiOHDnyqY95enoSERGBt7c3ERERjqI0T7JYLPz444/UqVOHatWqOe738vJy/L9Ro0b88MMPT32uxo0b07hxY8ffT+YJx8XF4erqiiVhR2cGpVKZqc8vPJtMJiM2NpaMGNqbVXPXhYwn9n3OJPZ7ziX2fc5Ux68OJz85yZBtQ5h/Yj7rLq5jTM0xtCzUMrNDyzbMNjO9NvYiyDWIL8p8kaW+RzKzmVxAfHAwsaGh6MLC8AbC4+KwhoZm2e/9s8YoZvrMoJUrV2bXrl0A7Nq1iypVqiRbRpIk5syZQ548eWjVqpXTYxERj4t4HD58mHz58r1wLFml61rI2sTnRBAEQRCEF+Gucee7Gt+x8e2N+Ov86bm9J3139CXCEPH8lQXmnJ7DxYiLjKs1Djf1iw01yyiSVoukVCJLHEb3GqSeZnpDsW3btpw+fZp+/fpx+vRp2rZtC0B4eDjjx48H4NKlS+zevZuzZ88yePBgBg8ezPHjxwF71dSvvvqKQYMGce7cObp165ZZL+WV6tq1K1FRUc9cZtKkSezevfuFtr9//34+/PDDVC//IlVn0zsGQRAEQRCE7KC8f3k2td3EoDcHsfH6RhqtbsSOOzsyO6ws7UbUDaYcn0LLQi1pWqBpZoeTnEyG5ObmGKMoMxgAewMyu8r0Yjbu7u588803ye738fFh2LBhAJQsWZI///wzxfW/+OKLDI0vq5EkCUmS+P3335+77ODBg19BRHYbNmx4Zc8lCIIgCIKQ3SnlSgZUGkDj/I3p/19/um7pSueSnfmm2jdZrrcss1ltVr7a/RVquZrvanz3/BUyic3Dw9GjKNPbpznLzg3FTO9RFJzNmTOHhg0b0rBhQ+bPnw/AnTt3qFevHsOGDaNZs2bcv3+fatWqER4eDsCUKVOoW7cuH3zwAX369GHOnDkAfPnll2zcuBGAatWqMXnyZJo1a0ajRo24evUqACdOnKBNmzY0bdqUNm3aOO5/mkuXLtGyZUuaNGlC48aNuX79OgDFihUDwGazMWzYMBo0aMCHH35I165d0z0GQRAEQRCE18Ubfm+wue1mepfrzR8X/6DZ2macDjmd2WFlKfPPzudQ8CHG1ByTpeeFltzckIkexdefxzffoDp/Pl23aS5dmujvnn4V5PTp0yxbtoyNGzciSRKtWrWiRo0aeHp6cu3aNX766SdHOm6iU6dOsXnzZrZu3YrVaqVZs2aUK1cuxe37+PiwdetWfv31V+bMmcPkyZMpWrTo/9u797gqq7T/45994IwQKEoqVpZWnoBRPGWaBzxlqcRYjpqa01Rimo6TmjJNqQ+amqdMe+YlRuZUjqOok3kufVJL1J/WmJogmUcUURQEYe99//4w90sSHDF0A/v7/gv2vg/Xva97ARdr3WuxfPlyrFYrW7duZerUqc4CtTiLFy9myJAhxMTEUFBQgN1uL/L+mjVrOH78OJs2bSIzM5MnnniCZ599tkxjEBEREalMvK3eTGgxgU51OjHsy2E8veppxkWN48XGL2I2uXe/zsGsg0xNmUq3+7sRWy/W1eHclCMgAPO1dRTz8zE8PMBaccutiht5JbRz5066d+/uXPqjW7dufPvtt3Tu3JnatWvTtOmNa+7s3LmTLl264OPjA0B0dHSJx+/WrRsATZo04YsvvgDg4sWLvPbaa6Snp2MymSgsLLxpjE2bNmXOnDmcOnWKbt26Ubdu3Rvi6dGjB2azmerVq9O6desyj0FERESkMmp5b0vWx6xn9NbRvP3t23x98mtmtZtFVZ+yX5arIiiwFzD8q+EEeAUwtc3Ucj+hoOHvj/n0aeDq0NOK3JsIKhRLdLOevzvlZksuXCseS7PPr11bn9FisTh7AqdNm0br1q1ZuHAhx44dc65JWZLevXsTGRnJpk2b6NevH9OmTaNNmzZ3NQYRERGRyirYO5iF0QtJ+iGJt799m+jl0cx5Yg5tat3631uVRUJKAvvP7ScxOrFCFMuOgACshw8Dv/QoVvBC0b37ssuZli1b8sUXX5CXl8fly5dZu3ZtkTUji9O8eXM2bNhAfn4+ubm5bNq0qVTnvHTpEqGhV8d6lzRh0PWOHj3Kfffdx5AhQ4iOjubAgQNF3o+KiuLzzz/H4XBw9uxZduzYUeYxiIiIiFRmJpOJQQ0Hsbrnavw9/HluzXNMSZmCzeE+a21/kf4F//v9/zK4wWC63N/F1eHcEsPfH9O1oad5eRi/jPirqFQoliONGzfmueee48knn6RHjx707duXRo0a3XSfiIgIOnfuTHR0NH/84x8JDw+nSpUqt3zOV155hYSEBHr27HnD84bFWbVqFR06dCA6Opq0tLQbev+efPJJ7r33Xjp06MCYMWOIjIwkICCgTGMQERERcQcNqzZkbe+1PFv/Webuncsz/36G45eOuzqsO+7oxaOM2jqK8GrhxLeMd3U4t8xRpUqR5TEqeo+iySjN2MVK5uTJk0W+v3z5colDPO8Wq9WKzVa6/xbl5ubi5+dHXl4eMTExvPPOOzRu3PgORXjr8WRlZdGjRw+Sk5OpXr26y+Ipa3fqPqlWrRqZmZllflwp/5R796S8uy/l3n39ltyvTFvJ6//3OhaThWltp/HkA0+WcXTlQ54tj5jVMRy9eJS1vddSJ6COq0O6Zf5z5xIwZQon09IIfvFFzJmZZP4yJ0d5bfc1a9Ys8T09o1gJvP766/z4449cuXKF3//+9y4tEgEGDhxIdnY2hYWFjBgxolIViSIiIiKu0PPBnoSHhBO3OY4/bfwT/R/pz99a/Q0fa8Ue3ng9wzAYvXU032V+x6LOiypUkQhXexQBzDk5msxGyod58+a5OoQili1b5uoQRERERCqd+wPuZ8VTK3hn1zvM/24+uzJ28X6H93k4+GFXh1YmZv2/WSSnJTM2aiyd7+vs6nBKzfilUDRdvIgpP99ZOFZUekZRRERERKSC8LR4MqHFBJZ0XUJmfibdk7vz8YGPSzUTfnm0Mm0l03dPJ7ZeLMPCh7k6nNtSpEcxP1+T2YiIiIiIyN31RNgTbIjZQFRoFGO+HsPLm14m+0q2q8O6LV8e+5IRX42geY3mvPP4O+V+vcSS/LpHsaIPPVWhKCIiIiJSAVX3rc4/uv2DN6LeYO1Pa+m8vDO7Mna5OqxS+fbUt/xxwx+pH1SfD7t8iJfFy9Uh3bZfP6OICkUREREREXEFs8lMXEQcy59ajgkTvVf3JmFnAvm2fFeH9l+lZKQwcN1AavnX4h/d/kGgV6CrQ/pNDH9/4JcexdxcHL98X1GpUJQbzJkzp8yOlZ2dzYcffljq/WbMmMGCBQtuadt9+/YRH1/2a+yUJgYRERERV2paoynrn1nPs/Wf5b1979FtRTf2nt3r6rBKtOX4Fvqu6Us1n2p82v1TqvlUc3VIv5nxy9rh5osXMeXkOIeiVlQqFMspwzBwOBwuOffcuXOLff12Yrp48SIfffRRWYRVovDwcCZOnHhHzyEiIiJS3gV4BjC97XQ+7voxlwov8dTKp0jYmUCeLc/VoRWRnJrMwHUDeSDgAVY8tYKa/iWv5VeRXOtBNGdkYDKMCj/rqcuXx8jJyWHmzJmcPXuWkJAQRo4ciX8x3bRxcXF4e3tjNpuxWCxMmTKlVPtXBMeOHWPAgAG0atWK3bt3k5iYyOrVq1m9ejUFBQV07dqV0aNHA/DPf/6TDz74AIBHH32UuXPncvz4cUaNGkVWVhbBwcHMnDmTWrVq8dprr1GlShX27dvH2bNnGT9+PD169CAjI4NXXnmFS5cuYbfbSUhIYNOmTeTn5xMdHc3DDz/MmDFj6N+/P61bt3bG1L59ew4fPgzAv//9bzZu3MisWbM4e/YsY8eO5ejRowAkJCSQmJjI0aNHiY6Opm3btsTHxzN//vxir2n27NksW7aMmjVrUrVqVZo0aXLDZ7R69WpmzpyJ2WwmICCA5cuXs337dhYsWMBHH33EuXPniIuL4/z584SHh/PVV1+xdu1acnNz6d+/P82bN2fXrl2EhoaSmJiIj48PS5YsYcmSJRQUFPDAAw8wZ84cfCr4LFUiIiLivtqHtWdz7Gbe2vEW7+17j1VHVvFWq7dcvuSE3WHnnV3v8N6+92gZ2pLEzokVfrhpEZ6eGN7eWE6eBKjwPYouLxSTk5Np3LgxvXr1Ijk5meTkZPr371/stm+++SYBv3Tp3s7+pfHXHX/lh3M//ObjXK9B1Qa83ertm26TmprKjBkzSEhIYMuWLaSnp/P5559jGAaDBg3im2++ISgoiDlz5rBy5UqCg4M5f/48AOPHjyc2NpY+ffrw6aefEh8fT2JiIgAZGRkkJyeTmprK4MGD6dGjBytWrKBdu3aMGDECu91OXl4eLVq0YNGiRWzYsAG4WrympaXx7rvvkpCQcNPY4+PjadmyJQsXLsRut5Obm8sbb7zBoUOHnMcr6Zp8fX1ZtWoV69evx2az0bVr12ILxVmzZrFkyRLuvfdesrNvnNnr3Xff5bHHHuPVV1/lyy+/ZMmSJc730tPTmTdvHtOmTeOll15izZo1PPPMM3Tr1o1+/foBMHXqVD755BNeeOGFm16riIiISHkW4BnAjHYziKkXw4RtExi8fjAdwzryVqu3eCDwgbsez5nLZxi1ZRRfHv+S/o/0Z2LriXhaPO96HHeaw98fy6lTzq8rMpcPPU1JSaFdu3YAtGvXjpSUlLu6f3lTu3ZtmjZtClwtqrZs2ULnzp3p0qULaWlppKens23bNp588kmCg4MBCAoKAmD37t307t0bgGeeeYadO3c6j9u1a1fMZjP169fn7NmzAERERLB06VJmzJjBgQMHSuyJvT6mm9m2bRvPP/88ABaL5Yai/mbX9O2339K1a1d8fHyoUqUK0dHRxZ6jWbNmjBw5kiVLlmC32294f+fOnfTs2ROA9u3bc8899zjfCwsLo1GjRgA0adKEY8eOAXDo0CF69+5Nx44dWbFiBYcOHfqv1yoiIiJSETxW8zHWP7Oe+BbxfHP6G5745xO8/n+vczLn5F2LYU36Gjos68COUzuY0mYKUx+fWimLRLjai+jsUSzmb+GKxOU9itnZ2c5CJygoiIsXL5a47eTJkwGIjo6mU6dOpd5/48aNbNy4EYApU6ZQrVrRh2YzMjKwWq9+JP/z+P/c5hXdPovFgq+vrzMGk8nEiBEjnMXXNX//+9+xWCzO7a4xmUxYrVasViuGYWA2m7FarZjNZnx8fJzbG4aB1WqlTZs2rFy5kg0bNjBixAji4uLo06cPgHPbX8d0/XkAbDab8zzXn//6a7r+eCVd0wcffFDkmsxms/O415sxYwa7d+9m48aNdOnShU2bNmGxWIrE9OvPxmKxYLFY8PLycr7u4eFBQUEBVquVkSNHkpSURMOGDfn000/Zvn2783MrLgYvL68b7p2yYLVa78hxpfxT7t2T8u6+lHv35crcT+gwgSHNhzBl2xQW7l3IssPLeDHyRYY3H859gffdkXMeOneIv2z8C+uOrCMyNJJFTy3i0WqP3pFzlRfmoCBM+/YBEFC7NsYv+a6I7f6uFIoTJ07kwoULN7z+3HPPleoYwcHBZGdnM2nSJGrWrEmDBg1KFUenTp2cBSZAZmZmkfevXLniLGxc4VoPmc1mA6Bt27ZMmzaNnj174ufnx6lTp/Dw8KB169YMGTKEIUOGOIeeBgUF0bRpU/71r38RGxvL0qVLiYqKwmaz4XA4sNvtzuNeO8fx48cJDQ2lb9++5OTksHfvXmJiYvDw8CAvLw8PD48bYgIICQnhwIEDPPjgg3z++ef4+flhs9l47LHHSExM5MUXX8Rut3P58mW8vb3Jycn5r9fUvHlzRo4cySuvvILdbmfdunUMGDCgyHkBfvrpJ8LDwwkPD2fdunX8/PPP2O12DMPAZrMRFRVFcnIycXFxbNmyhQsXLmC322+4DofDgcPhwGazkZOTQ9WqVcnLy2PZsmWEhoY6P7dr21zvypUrN9w7ZaFatWp35LhS/in37kl5d1/Kvftyde498CC+aTyD6g9i5p6ZzN89n/d3v0/X+7syqMEgWt3bCrPptw84TL2QyoLvFrDs8DK8Ld7Et4jnhYYv4Ilnpb/3q/r44PXL353n7XZsv1yvq3Nfkpo1S55I6K4UijdbuiAwMNBZ6Jw/f77Y4YqAc5hlYGAgUVFRpKam0qBBg1vevyJq164dhw8f5umnnwbA19eXuXPn8vDDDzN8+HBiY2Mxm800atSIWbNmMXHiREaNGsWCBQuck9nczLVJYKxWK35+fsyePRuAfv360alTJxo3bsyYMWNu2G/cuHEMHDiQmjVr8vDDD5ObmwvA22+/zeuvv86nn36K2WwmISGBZs2aERUVRYcOHWjfvj3x8fHFXlPjxo156qmn6Ny5M7Vr16ZFixbFxjxp0iTS09MxDIM2bdrQsGFDduzY4Xx/1KhRDB06lFWrVtGyZUtq1KiBn5+fM8bi/OUvf6FHjx7Url2bRx55hJycnJt+biIiIiIVWViVMN5t9y5/bvpnkn5IYsnBJaxJX0OoXyhP132abvd3IyIkolTDQy8VXGLtT2tJTktmy/EteFm8+MMjf2Bk5EhCfEPu4NWUL9fPdFrRZz01GYZhuDKAxYsXU6VKFedkNDk5OTdMRpOfn49hGPj4+JCfn8+kSZOIjY0lIiLilvYvycmTRcdmX758GV9f3zK7ttthtVpv6MGSW3etV9hqtbJr1y7GjRvnnEinrNyp+6S8/qdJ7jzl3j0p7+5LuXdf5TX3ebY81h9dz4rUFXx1/CsKHYX4WH1oWr0pjwY/Sr2getTwrcE9Xvfgafbkiv0KFwsucuzSMY5kHyElI4X95/ZjN+yE+YcRWz+WQQ0GVYq1EUsrcOxY/BYvBuDkkSPg5QWU39y7vEfxZnr16sXMmTPZvHkz1apVY9SoUQBkZWXxwQcfMG7cOLKzs5k+fTpwdXhmmzZtiIiIuOn+4p5OnDjByy+/jMPhwNPTk2nTprk6JBEREZFyzcfqQ88He9LzwZ5cuHKBHSd3sP3Udnae3sniA4vJt+eXuK+3xZvI6pEMixhGh7AONK3eFJPJdBejL1/s994LgMPHx1kkVlQu71F0JfUoyu1Qj6KUNeXePSnv7ku5d18VMfcOw8GJnBNk5mVy/sp5bA4bXhYvfD18qVOlDiE+IWXyXGNl4b1yJcFDh2J4enIqPd35ennNfbnuURQRERERkfLJbDITViWMsCphrg6lQiho0QLDw4PcSrAmtwrF67hx56qUgu4TERERESmOIzSU0/v3Y3h7uzqU30z9xNcxm80a9ik3dW3dSBERERGR4hh+fuDCJffKinoUr+Pt7U1+fj5Xrlxx2UO4Xl5eXLlyxSXnlpszDAOz2Yx3JfgPkYiIiIjIzahQvI7JZMLHx8elMZTXB11FRERERMR9aAydiIiIiIiIFKFCUURERERERIpQoSgiIiIiIiJFmAzN9S8iIiIiIiLXUY9iOTN27FhXhyAuoty7L+XePSnv7ku5d1/KvfuqiLlXoSgiIiIiIiJFqFAUERERERGRIlQoljOdOnVydQjiIsq9+1Lu3ZPy7r6Ue/el3Luviph7TWYjIiIiIiIiRahHUURERERERIqwujoAuWrv3r0sWrQIh8NBx44d6dWrl6tDkjIWFxeHt7c3ZrMZi8XClClTyMnJYebMmZw9e5aQkBBGjhyJv78/ACtWrGDz5s2YzWYGDx5MRESEay9Abtn777/Pnj17CAwMZMaMGQC3lesjR44wb948CgoKiIyMZPDgwZhMJlddltyC4nK/dOlSNm3aREBAAAB9+/bld7/7HaDcVxaZmZnMmzePCxcuYDKZ6NSpE927d1e7dwMl5V7tvvIrKCjgzTffxGazYbfbadmyJX369Klc7d4Ql7Pb7cawYcOM06dPG4WFhcbo0aONY8eOuTosKWNDhw41srOzi7y2ePFiY8WKFYZhGMaKFSuMxYsXG4ZhGMeOHTNGjx5tFBQUGBkZGcawYcMMu91+t0OW27R//34jLS3NGDVqlPO128n12LFjjUOHDhkOh8OYPHmysWfPnrt+LVI6xeX+s88+M1auXHnDtsp95ZGVlWWkpaUZhmEYly9fNoYPH24cO3ZM7d4NlJR7tfvKz+FwGHl5eYZhGEZhYaExbtw449ChQ5Wq3WvoaTmQmppKaGgoNWrUwGq10rp1a1JSUlwdltwFKSkptGvXDoB27do5856SkkLr1q3x8PCgevXqhIaGkpqa6spQpRQaNGjg/O/hNaXN9fnz58nLy6N+/fqYTCbatm2rnwsVQHG5L4lyX3kEBQVRt25dAHx8fKhVqxZZWVlq926gpNyXRLmvPEwmE97e3gDY7Xbsdjsmk6lStXsNPS0HsrKyqFq1qvP7qlWrcvjwYRdGJHfK5MmTAYiOjqZTp05kZ2cTFBQEXP1lc/HiReDqPVGvXj3nfsHBwTf9xSPlX2lzbbFYbvi5oHug4lq3bh1bt26lbt26PP/88/j7+yv3ldSZM2dIT0/noYceUrt3M9fn/uDBg2r3bsDhcDBmzBhOnz5Nly5dqFevXqVq9yoUywGjmIlny8W4ZClTEydOJDg4mOzsbCZNmkTNmjVL3La4e0Iqp5JyrXug8ujcuTOxsbEAfPbZZ3z00UcMHTpUua+E8vPzmTFjBoMGDcLX17fE7ZT7yufXuVe7dw9ms5lp06aRm5vL9OnT+fnnn0vctiLmXkNPy4GqVaty7tw55/fnzp1z/idCKo/g4GAAAgMDiYqKIjU1lcDAQM6fPw/A+fPnnQ+9//qeyMrKcu4vFVNpc13czwXdAxXTPffcg9lsxmw207FjR9LS0gDlvrKx2WzMmDGDxx9/nBYtWgBq9+6iuNyr3bsXPz8/GjRowN69eytVu1ehWA48+OCDnDp1ijNnzmCz2di+fTvNmjVzdVhShvLz88nLy3N+/d1331GnTh2aNWvGli1bANiyZQtRUVEANGvWjO3bt1NYWMiZM2c4deoUDz30kMvil9+utLkOCgrCx8eHH3/8EcMw2Lp1q34uVFDX/mAA2LlzJ2FhYYByX5kYhsGCBQuoVasWPXr0cL6udl/5lZR7tfvK7+LFi+Tm5gJXZ0D9/vvvqVWrVqVq9yajPPd3upE9e/aQlJSEw+Ggffv2xMTEuDokKUMZGRlMnz4duPrAc5s2bYiJieHSpUvMnDmTzMxMqlWrxqhRo5wTYSxfvpwvv/wSs9nMoEGDiIyMdOUlSCnMmjWLH374gUuXLhEYGEifPn2Iiooqda7T0tJ4//33KSgoICIighdeeEHD0su54nK/f/9+fvrpJ0wmEyEhIfzpT39yjhpR7iuHgwcP8te//pU6deo489S3b1/q1aundl/JlZT7bdu2qd1XckePHmXevHk4HA4Mw6BVq1bExsbe1t925TX3KhRFRERERESkCA09FRERERERkSJUKIqIiIiIiEgRKhRFRERERESkCBWKIiIiIiIiUoQKRRERERERESlChaKIiIiIiIgUoUJRRETkVzIzMxkwYAAOh+OOnysuLo5+/foxd+7cO3aOefPm0a9fP15++eU7dg4REalcVCiKiIjbi4uL47vvvnN+X61aNRYvXozZfHd+TY4ZM4ZXX331jh0/Li6ON954444dX0REKh8ViiIiIiIiIlKE1dUBiIiIuNLcuXPJzMxk6tSpmM1mYmNjadWqFcOGDeOTTz7BYrHwt7/9jUceeYT//Oc/HD16lIYNGxIXF8eiRYvYvXs3NWvWZOTIkVSvXh2AEydOkJiYyJEjRwgICODZZ5+ldevWtxSPYRgkJSXx9ddfU1hYSEhICMOHD6dOnToUFhbyySefsGPHDmw2G1FRUQwaNAhPT08AUlJSWLp0KWfOnCEgIIAhQ4YQERFxpz46ERGpxFQoioiIW3v11Vc5ePAgL730Ek2aNAHgzJkzN2y3bds2xo8fT0BAAOPHj2fChAkMGTKEuLg45s+fz7Jlyxg6dCj5+flMmjSJPn368MYbb3D06FEmT55MWFgYYWFh/zWeffv2ceDAAWbPno2vry8nTpzAz88PgCVLlpCRkcG0adOwWCzMnj2bZcuW8Yc//IHU1FTee+89/vznP9OoUSMuXLhAXl5e2X5YIiLiNjT0VERE5Ba0b9+e0NBQfH19iYyMpEaNGjRp0gSLxULLli1JT08HYM+ePYSEhNC+fXssFgt169alRYsWfPPNN7d0HqvVSn5+PidOnMAwDGrXrk1QUBCGYbBp0yYGDhyIv78/Pj4+xMTEsG3bNgA2b95M+/btadKkCWazmeDgYGrVqnXHPg8REanc1KMoIiJyCwIDA51fe3p63vB9fn4+AGfPnuXw4cMMGjTI+b7dbqdt27a3dJ5GjRrRpUsXFi5cSGZmJs2bN2fAgAEUFhZy5coVxo4d69zWMAznzKznzp0jMjLyt1yiiIiIkwpFERGRMlS1alUaNGhAfHz8bR+je/fudO/enezsbGbOnMmqVavo06cPnp6evPvuuwQHBxd73tOnT/+W0EVERJw09FRERNzePffcU+xzibejadOmnDp1iq1bt2Kz2bDZbKSmpnL8+PFb2j81NZXDhw9js9nw8vLCw8MDs9mM2WymY8eOfPjhh2RnZwOQlZXF3r17AejQoQNfffUV33//PQ6Hg6ysLE6cOFEm1yQiIu5HPYoiIuL2evXqRWJiIh9//DExMTG0bNnyto/l4+PDhAkTSEpKIikpCcMwuO+++xg4cOAt7Z+Xl0dSUhIZGRl4enoSHh7O008/DUC/fv1YtmwZ48eP59KlSwQHBxMdHU1ERAQPPfQQQ4cOJSkpiTNnzhAYGMiQIUP0nKKIiNwWk2EYhquDEBERcVcjRozgwoULREVFMWzYsDtyjvnz5/PNN98QEBDA3Llz78g5RESkclGhKCIiIiIiIkXoGUUREREREREpQoWiiIiIiIiIFKFCUURERERERIpQoSgiIiIiIiJFqFAUERERERGRIlQoioiIiIiISBEqFEVERERERKSI/w96jK4Keo/ATgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cell 5: reconstruct the function using the code in cell 2\n",
+    "g = series_real_coeff(a0, a, b, t, dt)\n",
+    "\n",
+    "# plotting\n",
+    "#plt.plot(t, sig, 'r', label='original signal')          # NOT normalized \n",
+    "#plt.plot(t, g, 'g', label='reconstructed signal')\n",
+    "plt.plot(t, sig/max(sig), 'r', label='original signal')  # normalized \n",
+    "plt.plot(t, g/max(g), 'g', label='reconstructed signal')\n",
+    "\n",
+    "plt.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
+    "plt.xlabel('time [sec]')\n",
+    "plt.ylabel('amplitude')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### Fourier series, convergence and Gibb's phenomenon\n",
+    "\n",
+    "As seen above the convergence of the *Fourier series* can be tricky due to the fact that we work with signals of finite length. To analyse this effect in a bit more detail, we define a square wave in cell 6 and try to reconstruct it in cell 7.  \n",
+    "5) First, we use only 5 coefficients to reconstruct the wave. Describe what you see.  \n",
+    "6) Increase the number of coefficients $n$ in cell 7 step by step and re-run the cell.  What do you see now? Can you explain it?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cell 6: define a square wave of 5 Hz\n",
+    "freq = 5.\n",
+    "npts = 500\n",
+    "dt_ = 0.002\n",
+    "length = npts * dt_\n",
+    "t_ = np.linspace(0, length, npts, endpoint=False)\n",
+    "square = signal.square(2 * np.pi * freq * t_)\n",
+    "\n",
+    "plt.plot(t_, square)\n",
+    "plt.xlabel('time [sec]')\n",
+    "plt.ylabel('amplitude')\n",
+    "plt.xlim(0, 1.05)\n",
+    "plt.ylim(-1.2, 1.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cell 7: reconstruct signal using convergence criterion\n",
+    "n = 5                 # number of coefficients (initial: 5)\n",
+    "T_ = 1/freq           # period of signal\n",
+    "\n",
+    "# determine coefficients\n",
+    "a0 = 0\n",
+    "a = []\n",
+    "b = []\n",
+    "for i in range(1,n):\n",
+    "    if (i%2 != 0):\n",
+    "        a_ = 4/(np.pi*i)\n",
+    "    else:\n",
+    "        a_ = 0\n",
+    "    a.append(a_)\n",
+    "    b_ = (2*np.pi*i)/T_\n",
+    "    b.append(b_)\n",
+    "\n",
+    "# reconstruct signal\n",
+    "g = np.ones_like(t_) * a0\n",
+    "for k, (ak, bk) in enumerate(zip(a, b)):\n",
+    "    g += ak * np.sin(bk*t_)\n",
+    "\n",
+    "# plotting\n",
+    "plt.plot(t_, square, 'r', label='original signal')                  \n",
+    "plt.plot(t_, g, 'g', label='reconstructed signal')\n",
+    "plt.ticklabel_format(axis='y', style='sci', scilimits=(-1,1))\n",
+    "plt.xlabel('time [sec]')\n",
+    "plt.ylabel('amplitude')\n",
+    "#plt.ylim(-1.1,1.1)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### Fourier transformation\n",
+    "\n",
+    "Let us now do the Fourier transformation of the signal created in cell 3 and have a look on the amplitude spectra. In computer science the transformation is performed as fast Fourier transformation (FFT).    \n",
+    "\n",
+    "7) Why do we need to taper the signal before we perform the FFT?  \n",
+    "8) How do you interpret the plot of the amplitude spectra?  \n",
+    "9) Which frequency contributes most to the final signal?   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "samp = 3000  Need to be the same as in cell 3.\n",
+      "T = 1  Need to be the same as in cell 3.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amplitude')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cell 8: FFT of signal\n",
+    "# number of sample points need to be the same as in cell 3\n",
+    "print('samp =',samp,' Need to be the same as in cell 3.')\n",
+    "# number of sample points need to be the same as in cell 3\n",
+    "print('T =',T,' Need to be the same as in cell 3.')\n",
+    "# percentage of taper applied to signal (initial: 0.1)\n",
+    "taper_percentage = 0.1\n",
+    "taper = cosine_taper(samp,taper_percentage)\n",
+    "\n",
+    "sig_ = sig * taper\n",
+    "Fsig = np.fft.rfft(sig_, n=samp)\n",
+    "\n",
+    "# prepare plotting\n",
+    "xf = np.linspace(0.0, 1.0/(2.0*T), (samp//2)+1)\n",
+    "rcParams[\"figure.subplot.hspace\"] = (0.8)\n",
+    "rcParams[\"figure.figsize\"] = (15, 9)\n",
+    "rcParams[\"axes.labelsize\"] = (15)\n",
+    "rcParams[\"axes.titlesize\"] = (20)\n",
+    "rcParams[\"font.size\"] = (12)\n",
+    " \n",
+    "#plotting\n",
+    "plt.subplot(311)\n",
+    "plt.title('Time Domain')\n",
+    "plt.plot(t, sig, linewidth=1)\n",
+    "plt.xlabel('Time [s]')\n",
+    "plt.ylabel('Amplitude')\n",
+    "\n",
+    "plt.subplot(312)\n",
+    "plt.title('Frequency Domain')\n",
+    "plt.plot(xf, 2.0/npts * np.abs(Fsig))\n",
+    "plt.xlim(0, 0.04)                                \n",
+    "plt.xlabel('Frequency [Hz]')\n",
+    "plt.ylabel('Amplitude')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/seismic/obsdata1.npy b/Notebooks/seismic/obsdata1.npy
similarity index 100%
rename from seismic/obsdata1.npy
rename to Notebooks/seismic/obsdata1.npy
diff --git a/seismic/syndata1.npy b/Notebooks/seismic/syndata1.npy
similarity index 100%
rename from seismic/syndata1.npy
rename to Notebooks/seismic/syndata1.npy
diff --git a/seismic/zeit.npy b/Notebooks/seismic/zeit.npy
similarity index 100%
rename from seismic/zeit.npy
rename to Notebooks/seismic/zeit.npy
diff --git a/dcip/.ipynb_checkpoints/DCIP_2D_Overburden_Pseudosections-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DCIP_2D_Overburden_Pseudosections-checkpoint.ipynb
deleted file mode 100644
index de72a7087d2c510ef919e3b1d6727e68b93c3c7e..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DCIP_2D_Overburden_Pseudosections-checkpoint.ipynb
+++ /dev/null
@@ -1,159 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from geoscilabs.dcip.DCIP_overburden_PseudoSection import (\n",
-    "    DCIP2DfwdWidget, DC2Dsimulation, PseudoSectionWidget, mesh\n",
-    ")\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Effects of a highly Conductive surface layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR)  or Induced Polarization (IP) survey, currents are injected to the earth, and flow. \n",
-    "Depending upon the conductivity and chargeability contrasts current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
-    "Here, we focus on a bloc target embedded in a halfspace below a highly conductive surface layer, and investigate what are happening in the earth when static currents are injected. The conductive layer will also impact the illumination of the target (conductor or resistor).\n",
-    "By investigating in data upon different physical properties contrasts, we explore the sensitivity of these surveys to the target."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/em/Dcapps_Overburden_draw.png?raw=true\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parameters\n",
-    " - **$\\rho_{1}$**: Resistivity of the half-space\n",
-    " - **$\\rho_{2}$**: Resistivity of the overburden\n",
-    " - **$\\rho_{3}$**: Resistivity of the target\n",
-    " - **$\\eta_{1}$**: Chargeability of the half-space\n",
-    " - **$\\eta_{2}$**: Chargeability of the overburden\n",
-    " - **$\\eta_{3}$**: Chargeability of the target\n",
-    " - **Overburden_thick**: thickness of the overburden\n",
-    " - **target_thick**: thickness of the target\n",
-    " - **target_wide**: width of the target\n",
-    " - **ellips_a**: x radius of ellipse\n",
-    " - **ellips_b**: z radius of ellipse\n",
-    " - **xc**: x location of ellipse center\n",
-    " - **zc**: z location of ellipse center\n",
-    " - **predmis**: Compare the Observed data to the ones without a target, see either the data (Overburden), or the difference between the two\n",
-    " - **Array Type**: Type of array\n",
-    " - **Rx per Tx**: How many receivers per sources\n",
-    " - **Survey**: DC or IP\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **When typing modifications to values, do not forget to PRESS ENTER**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "test = DCIP2DfwdWidget();\n",
-    "display(test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Appendix: Building Pseudosections \n",
-    "\n",
-    "2D profiles are often plotted as pseudo-sections by extending $45^{\\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\\Delta V_{MN}$, $\\rho_a$, or misfit value at the intersection of these lines as shown below. For pole-dipole or dipole-pole surveys the $45^{\\circ}$ line is simply extended from the location of the pole. By using this method of plotting, the long offset electrodes plot deeper than those with short offsets. This provides a rough idea of the region sampled by each data point, but the vertical axis of a pseudo-section is not a true depth.\n",
-    "\n",
-    "In the widget below the red dot marks the midpoint of the current dipole or the location of the A electrode location in a pole-dipole array while the green dots mark the midpoints of the potential dipoles or M electrode locations in a dipole-pole array. The blue dots then mark the location in the pseudo-section where the lines from Tx and Rx midpoints intersect and the data is plotted. By stepping through the Tx (current electrode pairs) using the slider you can see how the pseudo section is built up.\n",
-    "\n",
-    "The figures shown below show how the points in a pseudo-section are plotted for pole-dipole, dipole-pole, and dipole-dipole arrays. The color coding of the dots match those shown in the widget.\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/em/PoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img> \n",
-    "<center>Basic skematic for a uniformly spaced pole-dipole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/em/DipolePole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-pole array. \n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-dipole array.\n",
-    "<br />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surveyType = 'DipoleDipole'\n",
-    "simulation, xzlocs = DC2Dsimulation(np.ones(mesh.nC), surveyType)\n",
-    "PseudoSectionWidget(simulation, surveyType)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Building_Pseudosections-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Building_Pseudosections-checkpoint.ipynb
deleted file mode 100644
index bb41b22a722300e88b49946bb1815a1baae9ec25..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Building_Pseudosections-checkpoint.ipynb
+++ /dev/null
@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DC_Pseudosections import MidpointPseudoSectionWidget, DC2DPseudoWidget\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Building Pseudosections \n",
-    "\n",
-    "2D profiles are often plotted as pseudo-sections by extending $45^{\\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\\Delta V_{MN}$, $\\rho_a$, or misfit value at the intersection of these lines as shown below. For pole-dipole or dipole-pole surveys the $45^{\\circ}$ line is simply extended from the location of the pole. By using this method of plotting, the long offset electrodes plot deeper than those with short offsets. This provides a rough idea of the region sampled by each data point, but the vertical axis of a pseudo-section is not a true depth.\n",
-    "\n",
-    "In the widget below the red dot marks the midpoint of the current dipole or the location of the A electrode location in a pole-dipole array while the green dots mark the midpoints of the potential dipoles or M electrode locations in a dipole-pole array. The blue dots then mark the location in the pseudo-section where the lines from Tx and Rx midpoints intersect and the data is plotted. By stepping through the Tx (current electrode pairs) using the slider you can see how the pseudo section is built up.\n",
-    "\n",
-    "The figures shown below show how the points in a pseudo-section are plotted for pole-dipole, dipole-pole, and dipole-dipole arrays. The color coding of the dots match those shown in the widget.\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/Polepole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img> \n",
-    "<center>Basic skematic for a uniformly spaced pole-pole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/PoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img> \n",
-    "<center>Basic skematic for a uniformly spaced pole-dipole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipolePole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-pole array. \n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-dipole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3339a8be3c8f48588a37abbfcde855a0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(IntSlider(value=0, description='i', max=17), Output()), layout=Layout(align_items='stretch', d…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "out = MidpointPseudoSectionWidget()\n",
-    "display(out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " - **$\\rho_1$**: Resistivity of the halfspace\n",
-    " - **$\\rho_2$**: Resistivity of the cylinder\n",
-    " - **xc**: x location of cylinder center\n",
-    " - **zc**: z location of cylinder center\n",
-    " - **r**: radius of cylinder\n",
-    " - **surveyType**: Type of survey\n",
-    " - **Run Interact**: Use this button to update your plot\n",
-    " \n",
-    "\n",
-    " **Note:** The numerical results shown in this plot are generated from a 2d code such that the  source is a line of current. This greatly speeds up the computation. Accurate potentials obtained from point current sources require the 2.5D code.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b38cf8550b5499fab8724addbf7dbe9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=1000.0, description='$…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "out = DC2DPseudoWidget()\n",
-    "display(out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  },
-  "widgets": {
-   "state": {
-    "6e3f6835704641c7a49dfd241e063265": {
-     "views": [
-      {
-       "cell_index": 3
-      }
-     ]
-    },
-    "8be2bc831de14196a3a3189fddb922bb": {
-     "views": [
-      {
-       "cell_index": 2
-      }
-     ]
-    }
-   },
-   "version": "1.2.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Inversions-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Inversions-checkpoint.ipynb
deleted file mode 100644
index c1bafb6c3470616c24b050921f77252632075d6b..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Inversions-checkpoint.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DC_Pseudosections import DC2DfwdWidget\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Inverting Data\n",
-    "\n",
-    "In this final widget you are able to forward model the apparent resistivity of a cylinder embedded in an otherwise uniform halfspace. Pseudo-sections of the apparent resistivity can be generated using dipole-dipole, pole-dipole, or dipole-pole arrays to see how survey geometry can distort the size, shape, and location of conductive bodies in a pseudo-section.  Due to distortion and artifacts present in pseudo-sections trying to interpret them directly is typically difficult and dangerous due to the risk of misinterpretation. Inverting the data to find a model which fits the observed data and is geologically reasonable should be standard practice.   \n",
-    "\n",
-    "By systematically varying the model parameters and comparing the plots of observed vs. predicted apparent resistivity a parametric inversion can be preformed by hand to find the \"best\" fitting model. Normalized data misfits, which provide a numerical measure of the difference between the observed and predicted data, are useful for quantifying how well and inversion model fits the observed data. The manual inversion process can be difficult and time consuming even with small examples sure as the one presented here. Therefore, numerical optimization algorithms are typically utilized to minimized the data misfit and a model objective function, which provides information about the model structure and complexity, in order to find an optimal solution.\n",
-    "\n",
-    "Definition of variables:\n",
-    "- **$\\rho_1$**: Resistivity of the halfspace\n",
-    "- **$\\rho_2$**: Resistivity of the cylinder\n",
-    "- **xc**: x location of cylinder center\n",
-    "- **zc**: z location of cylinder center\n",
-    "- **r**: radius of cylinder\n",
-    "- **predmis**: toggle which allows you to switch the bottom pannel from predicted apparent resistivity to normalized data misfit\n",
-    "- **suveyType**: toggle which allows you to switch between survey types.\n",
-    "- **Run Interact**: Use this button to update your plot\n",
-    " \n",
-    " ###  **This app can be slow. You need to hit* Run Interact* to update the figure after you made modifications to the parameters**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0c0342348074acd9bee4257df7ce34a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=500.0, description='$\\\\rho_2…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "out = DC2DfwdWidget()\n",
-    "display(out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2_5D-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2_5D-checkpoint.ipynb
deleted file mode 100644
index de3081a50794c6b680e35eee91109a339db95953..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Layer_Cylinder_2_5D-checkpoint.ipynb
+++ /dev/null
@@ -1,153 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidgetResLayer2_5D import ResLayer_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams\n",
-    "rcParams['font.size'] = 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Effects of a highly resisitive surface layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
-    "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
-    "Here, we focus on a cylinder target embedded in a halfspace below a highly resistive surface layer, and investigate what are happening in the earth when static currents are injected. Different from a sphere case, which is a finite target, the resistive layer will also impact the illumination of the target (conductor or resistor).\n",
-    "By investigating changes in currents, electric fields, potential, and charges upon different geometry, Tx and Rx location, we understand geometric effects of the resistive layer for DCR survey. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DC_ResLayer_Setup.png?raw=true\"> </img>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Question\n",
-    "\n",
-    "- How does the cylinder affect the apparent resistivity without the resistive layer?\n",
-    "- How does the resistive layer affect the apparent resistivity? Is there a difference if you add or remove the cylinder target?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plate model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " - **survey**: Type of survey\n",
-    " - **A**: Electrode A (+) location\n",
-    " - **B**: Electrode B (-) location\n",
-    " - **M**: Electrode A (+) location\n",
-    " - **N**: Electrode B (-) location\n",
-    " - **$dz_{layer}$**: thickness of the resistive layer\n",
-    " - **$zc_{ayer}$**: z location of the resistive layer\n",
-    " - **xc**: x location of cylinder center\n",
-    " - **zc**: z location of cylinder center\n",
-    " - **$\\rho_{1}$**: Resistivity of the half-space\n",
-    " - **$\\rho_{2}$**: Resistivity of the layer\n",
-    " - **$\\rho_{3}$**: Resistivity of the cylinder\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **Do not forget to hit Run Interact to update the figure after you made modifications**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0ad9d0ce2b0a48659cbc06b458fa96c3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app = ResLayer_app()\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Overburden_2_5D-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Overburden_2_5D-checkpoint.ipynb
deleted file mode 100644
index d024be0c2b06b064ec9558df56340a85fa9b9568..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Overburden_2_5D-checkpoint.ipynb
+++ /dev/null
@@ -1,142 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidget_Overburden_2_5D import valley_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams\n",
-    "rcParams['font.size'] = 14"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Effects of a highly Conductive surface layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
-    "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
-    "Here, we focus on a bloc target embedded in a halfspace below a highly conductive surface layer, and investigate what are happening in the earth when static currents are injected. The conductive layer will also impact the illumination of the target (conductor or resistor).\n",
-    "By investigating changes in currents, electric fields, potential, and charges upon different geometry, Tx and Rx location, we understand geometric effects of the conductive layer for DCR survey. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/em/Dcapps_Overburden_draw.png?raw=true\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Question\n",
-    "\n",
-    "- How does the Target affect the apparent resistivity? Is there a difference if you add or remove the target?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overburden model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " - **survey**: Type of survey\n",
-    " - **A**: Electrode A (+) location\n",
-    " - **B**: Electrode B (-) location\n",
-    " - **M**: Electrode A (+) location\n",
-    " - **N**: Electrode B (-) location\n",
-    " - **$\\rho_{1}$**: Resistivity of the half-space\n",
-    " - **$\\rho_{2}$**: Resistivity of the overburden\n",
-    " - **$\\rho_{3}$**: Resistivity of the target\n",
-    " - **Overburden_thick**: thickness of the overburden\n",
-    " - **target_thick**: thickness of the target\n",
-    " - **target_wide**: width of the target\n",
-    " - **whichprimary**: which model to consider as primary: either uniform background or Overburden model\n",
-    " - **ellips_a**: x radius of ellipse\n",
-    " - **ellips_b**: z radius of ellipse\n",
-    " - **xc**: x location of ellipse center\n",
-    " - **zc**: z location of ellipse center\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **When typing modifications to values, do not forget to PRESS ENTER**\n",
-    "###  **Do not forget to hit Run Interact to update the figure after you made modifications**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "app = valley_app()\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Plate2_5D-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Plate2_5D-checkpoint.ipynb
deleted file mode 100644
index 549588da16980062f73b5b6bc5ed922763f9f069..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Plate2_5D-checkpoint.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidgetPlate2_5D import plate_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams\n",
-    "rcParams['font.size'] = 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected into the earth, and flow. \n",
-    "Depending upon the subsurface conductivity structures current flow in the earth will be distorted and charges will accumulate on interfaces between regions of different conductivites. These changes can be measurable at the sufurface electrodes. \n",
-    "\n",
-    "Here, we focus on a plate target embedded in a halfspace, and investigate what is happening in the earth when static currents are injected. Different from the sphere case, which is symmetric, \"coupling\" between the Tx, target (conductor or resistor), and Rx will be significanlty different with various scenarios and geometries. \n",
-    "Using this app we can investigate what effect different targets and survey geometries have on the currents, electric fields, potentials, charges, and sensitivities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Set up\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DC_PlateApp_Setup.png?raw=true\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questions\n",
-    "\n",
-    "- Is the potential difference measured by a dipole over a conductive (/resisitive) target higher or lower compared to the half-space reference?\n",
-    "- how do the field lines bend in presence of a conductive (/resistive) target?\n",
-    "- Compared to the positive and negative sources (A and B), how are oriented the positive and negative accumulated charges around a conductive (/resistive) target?\n",
-    "- How would you describe the secondary fields pattern? Does it remind you of the response of an object fundamental to electromagnetics?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plate app\n",
-    "\n",
-    "## Parameters:\n",
-    "\n",
-    " - **survey**: Type of survey\n",
-    " - **A**: (+) Current electrode  location\n",
-    " - **B**: (-) Current electrode  location\n",
-    " - **M**: (+) Potential electrode  location\n",
-    " - **N**: (-) Potential electrode  location\n",
-    " - **dx**: width of plate\n",
-    " - **dz**: height/thickness of plate\n",
-    " - **xc**: x location of plate center\n",
-    " - **zc**: z location of plate center\n",
-    " - **$\\theta$**: rotation angle of plate from the horizontal\n",
-    " - **$\\rho_1$**: Resistivity of the halfspace\n",
-    " - **$\\rho_2$**: Resistivity of the plate\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **Do not forget to hit Run Interact to update the figure after you made modifications**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b083a21e92e746dca7ae47a4e2e2773e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app = plate_app()\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Plate_2D-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Plate_2D-checkpoint.ipynb
deleted file mode 100644
index bce40d8415810de2d6cc1f0ce466b7f9782cba3a..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Plate_2D-checkpoint.ipynb
+++ /dev/null
@@ -1,111 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidgetPlate_2D import plate_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected into the earth, and flow. \n",
-    "Depending upon the subsurface conductivity structures current flow in the earth will be distorted and charges will accumulate on interfaces between regions of different conductivites. These changes can be measurable at the sufurface electrodes. \n",
-    "\n",
-    "Here, we focus on a plate target embedded in a halfspace, and investigate what is happening in the earth when static currents are injected. Different from the sphere case, which is symmetric, \"coupling\" between the Tx, target (conductor or resistor), and Rx will be significanlty different with various scenarios and geometries. \n",
-    "Using this app we can investigate what effect different targets and survey geometries have on the currents, electric fields, potentials, charges, and sensitivities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Set up\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/master/images/em/DC_PlateApp_Setup.png?raw=true\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questions\n",
-    "\n",
-    "- Is the potential difference measured by a dipole over a conductive (/resisitive) target higher or lower compared to the half-space reference?\n",
-    "- how do the field lines bend in presence of a conductive (/resistive) target?\n",
-    "- Compared to the positive and negative sources (A and B), how are oriented the positive and negative accumulated charges around a conductive (/resistive) target?\n",
-    "- How would you describe the secondary fields pattern? Does it remind you of the response of an object fundamental to electromagnetics?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plate app\n",
-    "\n",
-    "## Parameters:\n",
-    "\n",
-    " - **survey**: Type of survey\n",
-    " - **A**: (+) Current electrode  location\n",
-    " - **B**: (-) Current electrode  location\n",
-    " - **M**: (+) Potential electrode  location\n",
-    " - **N**: (-) Potential electrode  location\n",
-    " - **dx**: width of plate\n",
-    " - **dz**: height/thickness of plate\n",
-    " - **xc**: x location of plate center\n",
-    " - **zc**: z location of plate center\n",
-    " - **$\\theta$**: rotation angle of plate from the horizontal\n",
-    " - **$\\rho_1$**: Resistivity of the halfspace\n",
-    " - **$\\rho_2$**: Resistivity of the plate\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "app = plate_app();\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/.ipynb_checkpoints/DC_Sphere_Constant_E-checkpoint.ipynb b/dcip/.ipynb_checkpoints/DC_Sphere_Constant_E-checkpoint.ipynb
deleted file mode 100644
index facd50261cb3b17102c66c0633f98e0460adc933..0000000000000000000000000000000000000000
--- a/dcip/.ipynb_checkpoints/DC_Sphere_Constant_E-checkpoint.ipynb
+++ /dev/null
@@ -1,237 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.sphereElectrostatic_example import (\n",
-    "    interact_conductiveSphere, interactive_two_configurations_comparison\n",
-    ")\n",
-    "from IPython.display import display\n",
-    "from ipywidgets import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# **Conductive or Resistive Sphere in a wholespace with a constant, uniform electric field $E_0$**\n",
-    "\n",
-    "Parameters:\n",
-    "\n",
-    " - **Log_sig0** : log10 of the conductivity of the background (for example, a value of -5 means a conductivity of $10^{-5}$ S/m)\n",
-    " \n",
-    " - **Log_sig1** : log10 conductivity of the sphere\n",
-    " \n",
-    " - **$ R $**: radius of the sphere\n",
-    "     \n",
-    "The following example allows the user to plot any of the following physical values: \n",
-    "\n",
-    " - **Electric Potential** (**Total** or **Secondary**)\n",
-    " \n",
-    " - **Electric Field** (**Total** or **Secondary**)\n",
-    " \n",
-    " - **Current Density** (**Total** or **Secondary**)\n",
-    " \n",
-    " - **Charges density**\n",
-    " \n",
-    "To visualise configuration and primary potential, clic on \"Configuration\" (*Note that others buttons are then deactivated*)\n",
-    "\n",
-    "Buttons FigureX**a** allow to choose to plot either Total or Secondary Field.\n",
-    "\n",
-    "Buttons FigureX**b** allow to choose the physical value to plot.\n",
-    "\n",
-    "Please visit http://em.geosci.xyz/content/maxwell2_static/fields_from_grounded_sources_dcr/electrostatic_sphere.html for more information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7ad67ce6ca34006911562245bc1b880",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=50.0, description='R', max=50.0, step=10.0), FloatSlider(value=-3.0, d…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Function to visualise and compare any two plots for the same configuration\n",
-    "    \n",
-    "interact(interact_conductiveSphere,\n",
-    "        R=FloatSlider(min=0., max =50., step=10., value=50.),\n",
-    "        log_sig0=FloatSlider(min=-5., max =0., step=0.5,value=-3.),\n",
-    "        log_sig1=FloatSlider(min=-5., max =0., step=0.5,value=-1.),\n",
-    "        Figure1a=ToggleButtons(options=['Configuration','Total','Secondary'],value = 'Total'),\n",
-    "        Figure1b=ToggleButtons(options=['Potential','ElectricField','CurrentDensity','ChargesDensity'],value = 'ElectricField'),\n",
-    "        Figure2a=ToggleButtons(options=['Total','Secondary'],value = 'Secondary'),\n",
-    "        Figure2b=ToggleButtons(options=['Potential','ElectricField','CurrentDensity','ChargesDensity'],value = 'ElectricField'));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Building some intuition for DC problem"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In real life, we do not know the underground configuration. We only see the\n",
-    "data (in DCIP survey, Potentials difference between two electrodes) and we are trying to model the underground based from them. \n",
-    "\n",
-    "**There are several set of parameters that can fit perfectly a given data set**. Even in the simple\n",
-    "case presented here, where we know it is a sphere, and whose response can be calculated analytically, \n",
-    "we can find several configuration that can produce the same data along the same profile."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This code allow to plot and compare two differents configurations responses to the same survey.\n",
-    "\n",
-    "- **Log_sig0**: background log10 conductivity for both configurations\n",
-    "\n",
-    "- **Log_sig1**: sphere log10 conductivity in configuration 0\n",
-    "\n",
-    "- **Log_sig2**: sphere log10 conductivity in configuration 1\n",
-    "\n",
-    "- **R0**: Sphere's radius in configuration 0\n",
-    "\n",
-    "- **R1**: Sphere's radius in configuration 1\n",
-    "\n",
-    "- **E0**: uniform E field value\n",
-    "\n",
-    "- **start**: start point for the profile start.shape = (2,)\n",
-    "\n",
-    "- **end**: end point for the profile end.shape = (2,)\n",
-    "\n",
-    "- **dipole_number**: number of dipoles\n",
-    "\n",
-    "- **electrode_spacing**: Space between the M and N electrodes\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Are you able to find two spheres whose outside potentials are the same?**\n",
-    "\n",
-    "(one solution with \"matching_spheres_example\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1565415c288b44c3814c51e04f2b0b9c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=-3.0, description='log_sig0', max=0.0, min=-5.0, step=0.5), FloatSlide…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function geoscilabs.dcip.sphereElectrostatic_example.interactive_two_configurations_comparison(log_sig0, log_sig1, log_sig2, R0, R1, xstart, ystart, xend, yend, dipole_number, electrode_spacing, matching_spheres_example)>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Visualisation of the responses of two configurations to a (pseudo) DC resistivity survey\n",
-    "interact(interactive_two_configurations_comparison,\n",
-    "         R0=FloatSlider(min=0., max =50., step=10., value=50.),\n",
-    "         R1=FloatSlider(min=0., max =50., step=10., value=50.),\n",
-    "         log_sig0=FloatSlider(min=-5., max =0., step=0.5,value=-3.),\n",
-    "         log_sig1=FloatSlider(min=-5., max =0., step=0.5,value=-5.),\n",
-    "         log_sig2=FloatSlider(min=-5., max =0., step=0.5,value=-1.),\n",
-    "         xstart = FloatSlider(min=-200., max =200., step=10.,value=-200.),\n",
-    "         ystart = FloatSlider(min=-200., max =200., step=10.,value=100.),\n",
-    "         xend = FloatSlider(min=-200., max =200., step=10.,value=200.),\n",
-    "         yend = FloatSlider(min=-200., max =200., step=10.,value=100.),\n",
-    "         dipole_number = IntSlider(min=1, max=40, step=10,value=22),\n",
-    "         electrode_spacing = FloatSlider(min=0., max =100., step=5.,value=20.),\n",
-    "         matching_spheres_example = ToggleButton())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  },
-  "widgets": {
-   "state": {
-    "17ef604df6f34b91875cedb051f996e3": {
-     "views": [
-      {
-       "cell_index": 2
-      }
-     ]
-    },
-    "8a8a311386e349b191248f5b8a7780ba": {
-     "views": [
-      {
-       "cell_index": 7
-      }
-     ]
-    }
-   },
-   "version": "1.2.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/dcip/DCIP_2D_Overburden_Pseudosections.ipynb b/dcip/DCIP_2D_Overburden_Pseudosections.ipynb
deleted file mode 100644
index ced291ca01af614147d9fa92b8639bb5b04e8fab..0000000000000000000000000000000000000000
--- a/dcip/DCIP_2D_Overburden_Pseudosections.ipynb
+++ /dev/null
@@ -1,189 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from geoscilabs.dcip.DCIP_overburden_PseudoSection import (\n",
-    "    DCIP2DfwdWidget, DC2Dsimulation, PseudoSectionWidget, mesh\n",
-    ")\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Effects of a highly Conductive surface layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR)  or Induced Polarization (IP) survey, currents are injected to the earth, and flow. \n",
-    "Depending upon the conductivity and chargeability contrasts current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
-    "Here, we focus on a bloc target embedded in a halfspace below a highly conductive surface layer, and investigate what are happening in the earth when static currents are injected. The conductive layer will also impact the illumination of the target (conductor or resistor).\n",
-    "By investigating in data upon different physical properties contrasts, we explore the sensitivity of these surveys to the target."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/Dcapps_Overburden_draw.png?raw=true\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parameters\n",
-    " - **$\\rho_{1}$**: Resistivity of the half-space\n",
-    " - **$\\rho_{2}$**: Resistivity of the overburden\n",
-    " - **$\\rho_{3}$**: Resistivity of the target\n",
-    " - **$\\eta_{1}$**: Chargeability of the half-space\n",
-    " - **$\\eta_{2}$**: Chargeability of the overburden\n",
-    " - **$\\eta_{3}$**: Chargeability of the target\n",
-    " - **Overburden_thick**: thickness of the overburden\n",
-    " - **target_thick**: thickness of the target\n",
-    " - **target_wide**: width of the target\n",
-    " - **ellips_a**: x radius of ellipse\n",
-    " - **ellips_b**: z radius of ellipse\n",
-    " - **xc**: x location of ellipse center\n",
-    " - **zc**: z location of ellipse center\n",
-    " - **predmis**: Compare the Observed data to the ones without a target, see either the data (Overburden), or the difference between the two\n",
-    " - **Array Type**: Type of array\n",
-    " - **Rx per Tx**: How many receivers per sources\n",
-    " - **Survey**: DC or IP\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **When typing modifications to values, do not forget to PRESS ENTER**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d4418a577bdb43af800cc53441aa0143",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=100.0, description='$\\\\rho_2…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "test = DCIP2DfwdWidget();\n",
-    "display(test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Appendix: Building Pseudosections \n",
-    "\n",
-    "2D profiles are often plotted as pseudo-sections by extending $45^{\\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\\Delta V_{MN}$, $\\rho_a$, or misfit value at the intersection of these lines as shown below. For pole-dipole or dipole-pole surveys the $45^{\\circ}$ line is simply extended from the location of the pole. By using this method of plotting, the long offset electrodes plot deeper than those with short offsets. This provides a rough idea of the region sampled by each data point, but the vertical axis of a pseudo-section is not a true depth.\n",
-    "\n",
-    "In the widget below the red dot marks the midpoint of the current dipole or the location of the A electrode location in a pole-dipole array while the green dots mark the midpoints of the potential dipoles or M electrode locations in a dipole-pole array. The blue dots then mark the location in the pseudo-section where the lines from Tx and Rx midpoints intersect and the data is plotted. By stepping through the Tx (current electrode pairs) using the slider you can see how the pseudo section is built up.\n",
-    "\n",
-    "The figures shown below show how the points in a pseudo-section are plotted for pole-dipole, dipole-pole, and dipole-dipole arrays. The color coding of the dots match those shown in the widget.\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/PoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img> \n",
-    "<center>Basic skematic for a uniformly spaced pole-dipole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipolePole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-pole array. \n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-dipole array.\n",
-    "<br />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f03cac0367314845948fe94b0759b4ce",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(IntSlider(value=0, description='i', max=30), IntSlider(value=0, description='j', max=7), Toggl…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "surveyType = 'DipoleDipole'\n",
-    "simulation, xzlocs = DC2Dsimulation(np.ones(mesh.nC), surveyType)\n",
-    "PseudoSectionWidget(simulation, surveyType)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/dcip/DC_Building_Pseudosections.ipynb b/dcip/DC_Building_Pseudosections.ipynb
deleted file mode 100644
index 7d9b53af1f721b76c18141393c0aa1a4e5b02a3d..0000000000000000000000000000000000000000
--- a/dcip/DC_Building_Pseudosections.ipynb
+++ /dev/null
@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DC_Pseudosections import MidpointPseudoSectionWidget, DC2DPseudoWidget\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Building Pseudosections \n",
-    "\n",
-    "2D profiles are often plotted as pseudo-sections by extending $45^{\\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\\Delta V_{MN}$, $\\rho_a$, or misfit value at the intersection of these lines as shown below. For pole-dipole or dipole-pole surveys the $45^{\\circ}$ line is simply extended from the location of the pole. By using this method of plotting, the long offset electrodes plot deeper than those with short offsets. This provides a rough idea of the region sampled by each data point, but the vertical axis of a pseudo-section is not a true depth.\n",
-    "\n",
-    "In the widget below the red dot marks the midpoint of the current dipole or the location of the A electrode location in a pole-dipole array while the green dots mark the midpoints of the potential dipoles or M electrode locations in a dipole-pole array. The blue dots then mark the location in the pseudo-section where the lines from Tx and Rx midpoints intersect and the data is plotted. By stepping through the Tx (current electrode pairs) using the slider you can see how the pseudo section is built up.\n",
-    "\n",
-    "The figures shown below show how the points in a pseudo-section are plotted for pole-dipole, dipole-pole, and dipole-dipole arrays. The color coding of the dots match those shown in the widget.\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/Polepole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img> \n",
-    "<center>Basic skematic for a uniformly spaced pole-pole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/PoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img> \n",
-    "<center>Basic skematic for a uniformly spaced pole-dipole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipolePole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-pole array. \n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DipoleDipole.png?raw=true\" style=\"width: 60%; height: 60%\"> </img>\n",
-    "<center>Basic skematic for a uniformly spaced dipole-dipole array.\n",
-    "<br />\n",
-    "<br />\n",
-    "<br />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd9e198f0a8d49c1a253e3d62a37f180",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(IntSlider(value=0, description='i', max=17), Output()), layout=Layout(align_items='stretch', d…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "out = MidpointPseudoSectionWidget()\n",
-    "display(out)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " - **$\\rho_1$**: Resistivity of the halfspace\n",
-    " - **$\\rho_2$**: Resistivity of the cylinder\n",
-    " - **xc**: x location of cylinder center\n",
-    " - **zc**: z location of cylinder center\n",
-    " - **r**: radius of cylinder\n",
-    " - **surveyType**: Type of survey\n",
-    " - **Run Interact**: Use this button to update your plot\n",
-    " \n",
-    "\n",
-    " **Note:** The numerical results shown in this plot are generated from a 2d code such that the  source is a line of current. This greatly speeds up the computation. Accurate potentials obtained from point current sources require the 2.5D code.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4bcf46e21a77461ea388399a31f9e677",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=1000.0, description='$…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "out = DC2DPseudoWidget()\n",
-    "display(out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  },
-  "widgets": {
-   "state": {
-    "6e3f6835704641c7a49dfd241e063265": {
-     "views": [
-      {
-       "cell_index": 3
-      }
-     ]
-    },
-    "8be2bc831de14196a3a3189fddb922bb": {
-     "views": [
-      {
-       "cell_index": 2
-      }
-     ]
-    }
-   },
-   "version": "1.2.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/dcip/DC_Inversions.ipynb b/dcip/DC_Inversions.ipynb
deleted file mode 100644
index c1bafb6c3470616c24b050921f77252632075d6b..0000000000000000000000000000000000000000
--- a/dcip/DC_Inversions.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DC_Pseudosections import DC2DfwdWidget\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Inverting Data\n",
-    "\n",
-    "In this final widget you are able to forward model the apparent resistivity of a cylinder embedded in an otherwise uniform halfspace. Pseudo-sections of the apparent resistivity can be generated using dipole-dipole, pole-dipole, or dipole-pole arrays to see how survey geometry can distort the size, shape, and location of conductive bodies in a pseudo-section.  Due to distortion and artifacts present in pseudo-sections trying to interpret them directly is typically difficult and dangerous due to the risk of misinterpretation. Inverting the data to find a model which fits the observed data and is geologically reasonable should be standard practice.   \n",
-    "\n",
-    "By systematically varying the model parameters and comparing the plots of observed vs. predicted apparent resistivity a parametric inversion can be preformed by hand to find the \"best\" fitting model. Normalized data misfits, which provide a numerical measure of the difference between the observed and predicted data, are useful for quantifying how well and inversion model fits the observed data. The manual inversion process can be difficult and time consuming even with small examples sure as the one presented here. Therefore, numerical optimization algorithms are typically utilized to minimized the data misfit and a model objective function, which provides information about the model structure and complexity, in order to find an optimal solution.\n",
-    "\n",
-    "Definition of variables:\n",
-    "- **$\\rho_1$**: Resistivity of the halfspace\n",
-    "- **$\\rho_2$**: Resistivity of the cylinder\n",
-    "- **xc**: x location of cylinder center\n",
-    "- **zc**: z location of cylinder center\n",
-    "- **r**: radius of cylinder\n",
-    "- **predmis**: toggle which allows you to switch the bottom pannel from predicted apparent resistivity to normalized data misfit\n",
-    "- **suveyType**: toggle which allows you to switch between survey types.\n",
-    "- **Run Interact**: Use this button to update your plot\n",
-    " \n",
-    " ###  **This app can be slow. You need to hit* Run Interact* to update the figure after you made modifications to the parameters**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0c0342348074acd9bee4257df7ce34a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(FloatText(value=1000.0, description='$\\\\rho_1$'), FloatText(value=500.0, description='$\\\\rho_2…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "out = DC2DfwdWidget()\n",
-    "display(out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/DC_Layer_Cylinder_2_5D.ipynb b/dcip/DC_Layer_Cylinder_2_5D.ipynb
deleted file mode 100644
index e3d51d1df867f917dcb3b68c0db43180e9a74766..0000000000000000000000000000000000000000
--- a/dcip/DC_Layer_Cylinder_2_5D.ipynb
+++ /dev/null
@@ -1,153 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidgetResLayer2_5D import ResLayer_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams\n",
-    "rcParams['font.size'] = 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Effects of a highly resisitive surface layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
-    "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
-    "Here, we focus on a cylinder target embedded in a halfspace below a highly resistive surface layer, and investigate what are happening in the earth when static currents are injected. Different from a sphere case, which is a finite target, the resistive layer will also impact the illumination of the target (conductor or resistor).\n",
-    "By investigating changes in currents, electric fields, potential, and charges upon different geometry, Tx and Rx location, we understand geometric effects of the resistive layer for DCR survey. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DC_ResLayer_Setup.png?raw=true\"> </img>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Question\n",
-    "\n",
-    "- How does the cylinder affect the apparent resistivity without the resistive layer?\n",
-    "- How does the resistive layer affect the apparent resistivity? Is there a difference if you add or remove the cylinder target?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plate model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " - **survey**: Type of survey\n",
-    " - **A**: Electrode A (+) location\n",
-    " - **B**: Electrode B (-) location\n",
-    " - **M**: Electrode A (+) location\n",
-    " - **N**: Electrode B (-) location\n",
-    " - **$dz_{layer}$**: thickness of the resistive layer\n",
-    " - **$zc_{ayer}$**: z location of the resistive layer\n",
-    " - **xc**: x location of cylinder center\n",
-    " - **zc**: z location of cylinder center\n",
-    " - **$\\rho_{1}$**: Resistivity of the half-space\n",
-    " - **$\\rho_{2}$**: Resistivity of the layer\n",
-    " - **$\\rho_{3}$**: Resistivity of the cylinder\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **Do not forget to hit Run Interact to update the figure after you made modifications**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "86ea1e481db04bd198ed0ce844f59980",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app = ResLayer_app()\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/DC_Overburden_2_5D.ipynb b/dcip/DC_Overburden_2_5D.ipynb
deleted file mode 100644
index e7d5a73f8978d7f77093cddc7bf0a297927b7654..0000000000000000000000000000000000000000
--- a/dcip/DC_Overburden_2_5D.ipynb
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidget_Overburden_2_5D import valley_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams\n",
-    "rcParams['font.size'] = 14"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Effects of a highly Conductive surface layer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. \n",
-    "Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes \n",
-    "can be measurable on the sufurface electrodes. \n",
-    "Here, we focus on a bloc target embedded in a halfspace below a highly conductive surface layer, and investigate what are happening in the earth when static currents are injected. The conductive layer will also impact the illumination of the target (conductor or resistor).\n",
-    "By investigating changes in currents, electric fields, potential, and charges upon different geometry, Tx and Rx location, we understand geometric effects of the conductive layer for DCR survey. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/Dcapps_Overburden_draw.png?raw=true\" />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Question\n",
-    "\n",
-    "- How does the Target affect the apparent resistivity? Is there a difference if you add or remove the target?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overburden model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " - **survey**: Type of survey\n",
-    " - **A**: Electrode A (+) location\n",
-    " - **B**: Electrode B (-) location\n",
-    " - **M**: Electrode A (+) location\n",
-    " - **N**: Electrode B (-) location\n",
-    " - **$\\rho_{1}$**: Resistivity of the half-space\n",
-    " - **$\\rho_{2}$**: Resistivity of the overburden\n",
-    " - **$\\rho_{3}$**: Resistivity of the target\n",
-    " - **Overburden_thick**: thickness of the overburden\n",
-    " - **target_thick**: thickness of the target\n",
-    " - **target_wide**: width of the target\n",
-    " - **whichprimary**: which model to consider as primary: either uniform background or Overburden model\n",
-    " - **ellips_a**: x radius of ellipse\n",
-    " - **ellips_b**: z radius of ellipse\n",
-    " - **xc**: x location of ellipse center\n",
-    " - **zc**: z location of ellipse center\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **When typing modifications to values, do not forget to PRESS ENTER**\n",
-    "###  **Do not forget to hit Run Interact to update the figure after you made modifications**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c37bdce5224147579d46cedf2fbc19dc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app = valley_app()\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/DC_Plate2_5D.ipynb b/dcip/DC_Plate2_5D.ipynb
deleted file mode 100644
index 549588da16980062f73b5b6bc5ed922763f9f069..0000000000000000000000000000000000000000
--- a/dcip/DC_Plate2_5D.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidgetPlate2_5D import plate_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import rcParams\n",
-    "rcParams['font.size'] = 16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected into the earth, and flow. \n",
-    "Depending upon the subsurface conductivity structures current flow in the earth will be distorted and charges will accumulate on interfaces between regions of different conductivites. These changes can be measurable at the sufurface electrodes. \n",
-    "\n",
-    "Here, we focus on a plate target embedded in a halfspace, and investigate what is happening in the earth when static currents are injected. Different from the sphere case, which is symmetric, \"coupling\" between the Tx, target (conductor or resistor), and Rx will be significanlty different with various scenarios and geometries. \n",
-    "Using this app we can investigate what effect different targets and survey geometries have on the currents, electric fields, potentials, charges, and sensitivities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Set up\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DC_PlateApp_Setup.png?raw=true\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questions\n",
-    "\n",
-    "- Is the potential difference measured by a dipole over a conductive (/resisitive) target higher or lower compared to the half-space reference?\n",
-    "- how do the field lines bend in presence of a conductive (/resistive) target?\n",
-    "- Compared to the positive and negative sources (A and B), how are oriented the positive and negative accumulated charges around a conductive (/resistive) target?\n",
-    "- How would you describe the secondary fields pattern? Does it remind you of the response of an object fundamental to electromagnetics?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plate app\n",
-    "\n",
-    "## Parameters:\n",
-    "\n",
-    " - **survey**: Type of survey\n",
-    " - **A**: (+) Current electrode  location\n",
-    " - **B**: (-) Current electrode  location\n",
-    " - **M**: (+) Potential electrode  location\n",
-    " - **N**: (-) Potential electrode  location\n",
-    " - **dx**: width of plate\n",
-    " - **dz**: height/thickness of plate\n",
-    " - **xc**: x location of plate center\n",
-    " - **zc**: z location of plate center\n",
-    " - **$\\theta$**: rotation angle of plate from the horizontal\n",
-    " - **$\\rho_1$**: Resistivity of the halfspace\n",
-    " - **$\\rho_2$**: Resistivity of the plate\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization\n",
-    " \n",
-    "###  **Do not forget to hit Run Interact to update the figure after you made modifications**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b083a21e92e746dca7ae47a4e2e2773e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "MyApp(children=(ToggleButtons(description='survey', options=('Dipole-Dipole', 'Dipole-Pole', 'Pole-Dipole', 'P…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app = plate_app()\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/DC_Plate_2D.ipynb b/dcip/DC_Plate_2D.ipynb
deleted file mode 100644
index d375801f6d49e22f67375fca7ca7ab0f764c3c63..0000000000000000000000000000000000000000
--- a/dcip/DC_Plate_2D.ipynb
+++ /dev/null
@@ -1,111 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.DCWidgetPlate_2D import plate_app\n",
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Purpose \n",
-    "\n",
-    "For a direct current resistivity (DCR) survey, currents are injected into the earth, and flow. \n",
-    "Depending upon the subsurface conductivity structures current flow in the earth will be distorted and charges will accumulate on interfaces between regions of different conductivites. These changes can be measurable at the sufurface electrodes. \n",
-    "\n",
-    "Here, we focus on a plate target embedded in a halfspace, and investigate what is happening in the earth when static currents are injected. Different from the sphere case, which is symmetric, \"coupling\" between the Tx, target (conductor or resistor), and Rx will be significanlty different with various scenarios and geometries. \n",
-    "Using this app we can investigate what effect different targets and survey geometries have on the currents, electric fields, potentials, charges, and sensitivities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Set up\n",
-    "\n",
-    "<img src=\"https://github.com/geoscixyz/geosci-labs/blob/main/images/em/DC_PlateApp_Setup.png?raw=true\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questions\n",
-    "\n",
-    "- Is the potential difference measured by a dipole over a conductive (/resisitive) target higher or lower compared to the half-space reference?\n",
-    "- how do the field lines bend in presence of a conductive (/resistive) target?\n",
-    "- Compared to the positive and negative sources (A and B), how are oriented the positive and negative accumulated charges around a conductive (/resistive) target?\n",
-    "- How would you describe the secondary fields pattern? Does it remind you of the response of an object fundamental to electromagnetics?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plate app\n",
-    "\n",
-    "## Parameters:\n",
-    "\n",
-    " - **survey**: Type of survey\n",
-    " - **A**: (+) Current electrode  location\n",
-    " - **B**: (-) Current electrode  location\n",
-    " - **M**: (+) Potential electrode  location\n",
-    " - **N**: (-) Potential electrode  location\n",
-    " - **dx**: width of plate\n",
-    " - **dz**: height/thickness of plate\n",
-    " - **xc**: x location of plate center\n",
-    " - **zc**: z location of plate center\n",
-    " - **$\\theta$**: rotation angle of plate from the horizontal\n",
-    " - **$\\rho_1$**: Resistivity of the halfspace\n",
-    " - **$\\rho_2$**: Resistivity of the plate\n",
-    " - **Field**: Field to visualize\n",
-    " - **Type**: which part of the field\n",
-    " - **Scale**: Linear or Log Scale visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "app = plate_app();\n",
-    "display(app)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/dcip/DC_Sphere_Constant_E.ipynb b/dcip/DC_Sphere_Constant_E.ipynb
deleted file mode 100644
index facd50261cb3b17102c66c0633f98e0460adc933..0000000000000000000000000000000000000000
--- a/dcip/DC_Sphere_Constant_E.ipynb
+++ /dev/null
@@ -1,237 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from geoscilabs.dcip.sphereElectrostatic_example import (\n",
-    "    interact_conductiveSphere, interactive_two_configurations_comparison\n",
-    ")\n",
-    "from IPython.display import display\n",
-    "from ipywidgets import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# **Conductive or Resistive Sphere in a wholespace with a constant, uniform electric field $E_0$**\n",
-    "\n",
-    "Parameters:\n",
-    "\n",
-    " - **Log_sig0** : log10 of the conductivity of the background (for example, a value of -5 means a conductivity of $10^{-5}$ S/m)\n",
-    " \n",
-    " - **Log_sig1** : log10 conductivity of the sphere\n",
-    " \n",
-    " - **$ R $**: radius of the sphere\n",
-    "     \n",
-    "The following example allows the user to plot any of the following physical values: \n",
-    "\n",
-    " - **Electric Potential** (**Total** or **Secondary**)\n",
-    " \n",
-    " - **Electric Field** (**Total** or **Secondary**)\n",
-    " \n",
-    " - **Current Density** (**Total** or **Secondary**)\n",
-    " \n",
-    " - **Charges density**\n",
-    " \n",
-    "To visualise configuration and primary potential, clic on \"Configuration\" (*Note that others buttons are then deactivated*)\n",
-    "\n",
-    "Buttons FigureX**a** allow to choose to plot either Total or Secondary Field.\n",
-    "\n",
-    "Buttons FigureX**b** allow to choose the physical value to plot.\n",
-    "\n",
-    "Please visit http://em.geosci.xyz/content/maxwell2_static/fields_from_grounded_sources_dcr/electrostatic_sphere.html for more information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7ad67ce6ca34006911562245bc1b880",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=50.0, description='R', max=50.0, step=10.0), FloatSlider(value=-3.0, d…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Function to visualise and compare any two plots for the same configuration\n",
-    "    \n",
-    "interact(interact_conductiveSphere,\n",
-    "        R=FloatSlider(min=0., max =50., step=10., value=50.),\n",
-    "        log_sig0=FloatSlider(min=-5., max =0., step=0.5,value=-3.),\n",
-    "        log_sig1=FloatSlider(min=-5., max =0., step=0.5,value=-1.),\n",
-    "        Figure1a=ToggleButtons(options=['Configuration','Total','Secondary'],value = 'Total'),\n",
-    "        Figure1b=ToggleButtons(options=['Potential','ElectricField','CurrentDensity','ChargesDensity'],value = 'ElectricField'),\n",
-    "        Figure2a=ToggleButtons(options=['Total','Secondary'],value = 'Secondary'),\n",
-    "        Figure2b=ToggleButtons(options=['Potential','ElectricField','CurrentDensity','ChargesDensity'],value = 'ElectricField'));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Building some intuition for DC problem"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In real life, we do not know the underground configuration. We only see the\n",
-    "data (in DCIP survey, Potentials difference between two electrodes) and we are trying to model the underground based from them. \n",
-    "\n",
-    "**There are several set of parameters that can fit perfectly a given data set**. Even in the simple\n",
-    "case presented here, where we know it is a sphere, and whose response can be calculated analytically, \n",
-    "we can find several configuration that can produce the same data along the same profile."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This code allow to plot and compare two differents configurations responses to the same survey.\n",
-    "\n",
-    "- **Log_sig0**: background log10 conductivity for both configurations\n",
-    "\n",
-    "- **Log_sig1**: sphere log10 conductivity in configuration 0\n",
-    "\n",
-    "- **Log_sig2**: sphere log10 conductivity in configuration 1\n",
-    "\n",
-    "- **R0**: Sphere's radius in configuration 0\n",
-    "\n",
-    "- **R1**: Sphere's radius in configuration 1\n",
-    "\n",
-    "- **E0**: uniform E field value\n",
-    "\n",
-    "- **start**: start point for the profile start.shape = (2,)\n",
-    "\n",
-    "- **end**: end point for the profile end.shape = (2,)\n",
-    "\n",
-    "- **dipole_number**: number of dipoles\n",
-    "\n",
-    "- **electrode_spacing**: Space between the M and N electrodes\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Are you able to find two spheres whose outside potentials are the same?**\n",
-    "\n",
-    "(one solution with \"matching_spheres_example\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1565415c288b44c3814c51e04f2b0b9c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=-3.0, description='log_sig0', max=0.0, min=-5.0, step=0.5), FloatSlide…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function geoscilabs.dcip.sphereElectrostatic_example.interactive_two_configurations_comparison(log_sig0, log_sig1, log_sig2, R0, R1, xstart, ystart, xend, yend, dipole_number, electrode_spacing, matching_spheres_example)>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Visualisation of the responses of two configurations to a (pseudo) DC resistivity survey\n",
-    "interact(interactive_two_configurations_comparison,\n",
-    "         R0=FloatSlider(min=0., max =50., step=10., value=50.),\n",
-    "         R1=FloatSlider(min=0., max =50., step=10., value=50.),\n",
-    "         log_sig0=FloatSlider(min=-5., max =0., step=0.5,value=-3.),\n",
-    "         log_sig1=FloatSlider(min=-5., max =0., step=0.5,value=-5.),\n",
-    "         log_sig2=FloatSlider(min=-5., max =0., step=0.5,value=-1.),\n",
-    "         xstart = FloatSlider(min=-200., max =200., step=10.,value=-200.),\n",
-    "         ystart = FloatSlider(min=-200., max =200., step=10.,value=100.),\n",
-    "         xend = FloatSlider(min=-200., max =200., step=10.,value=200.),\n",
-    "         yend = FloatSlider(min=-200., max =200., step=10.,value=100.),\n",
-    "         dipole_number = IntSlider(min=1, max=40, step=10,value=22),\n",
-    "         electrode_spacing = FloatSlider(min=0., max =100., step=5.,value=20.),\n",
-    "         matching_spheres_example = ToggleButton())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  },
-  "widgets": {
-   "state": {
-    "17ef604df6f34b91875cedb051f996e3": {
-     "views": [
-      {
-       "cell_index": 2
-      }
-     ]
-    },
-    "8a8a311386e349b191248f5b8a7780ba": {
-     "views": [
-      {
-       "cell_index": 7
-      }
-     ]
-    }
-   },
-   "version": "1.2.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/index.ipynb b/index.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..15236887cdc8bf5e589a6e1cf7e352d60dfaa4e2
--- /dev/null
+++ b/index.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"http://em.geosci.xyz\"><img src=\"https://www.gge.eonerc.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaakevlz\" style=\"width: 25%; height: 25%\" align=\"right\"></img></a>\n",
+    "\n",
+    "# Jupyter Notebooks für die Veranstaltungen EdgE 1+2\n",
+    "\n",
+    "Die Jupyter Notebooks sind begleitendes Lehrmaterial für den RWTH Kurse \"Einführung in die Geophysikalische Erkundung 1+2\". Es werden verschiedene in den Vorlesungen unterrichtete Themen behandelt. Sämtliche Notebooks laufen direkt auf dem JupyterHub ohne die Notwendigkeit, Python auf dem eigenen Rechner installiert zu haben (einfach auf die jeweiligen Notebooks links in der Leiste klicken). Alternativ können die Notebooks auch lokal verwendet werden. Allerdings benötigt man hierfür gewisse geophysikalische Programme. Die Installation kann in der Dokumentation von **<a href=\"https://github.com/geoscixyz/geosci-labs \">Geoscilabs</a>**, den Urhebern der Notebooks nachgeschlagen werden.\n",
+    "\n",
+    "Weiterführende Literatur ist unten verlinkt:\n",
+    "- **<a href=\"http://gpg.geosci.xyz\">gpg.geosci.xyz</a>**, allgemein für angewandte Geophysik.\n",
+    "- **<a href=\"http://em.geosci.xyz\">em.geosci.xyz</a>**, speziell für elektromagnetische Themen.  \n",
+    "- **<a href=\"https://wiki.seg.org/wiki/Main_Page\">wiki.seg.org</a>**, das offizielle Wiki der  Society of Exploration Geophysicists (SEG).  \n",
+    "\n",
+    "\n",
+    "\n",
+    "Unten sind die betreffenden Notebooks aufgezählt inklusiver einer kurzen Erläuterung zu deren Inhalten. Die Notebooks können durch direktes anklicken der Links geöffnet werden. Bei weiteren Fragen, wendet euch an den Norbert eures Vertrauens (nklitzsch@eonerc.rwth-aachen.de). \n",
+    "\n",
+    "Viel Spaß mit den Notebooks!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[DCIP](#DC-Resistivity-und-Induced-Polarization) | [EMI](#Electromagnetics) | [GPR](#Ground-Penetrating-Radar-(GPR))  | [Magnetics](#Magnetics) | [Seismic](#Seismic) | [Gravity](#Gravity)** \n",
+    "\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "### DC Resistivity und Induced Polarization\n",
+    "- [DC_SurveyDataInversion.ipynb](./Notebooks/dcip/DC_SurveyDataInversion.ipynb): \n",
+    "\n",
+    "This Notebook introduces the fundamentals of DC resistivity surveys. It is divided into 4 major parts:\n",
+    "\n",
+    "1. Investigation of currents, fields, charges and potentials: All governing physical parameters are investigated in the environment of a cylinder target embedded in a homogeneous halfspace. Here, different subsurface characteristics such as cylinder and electrode geometry or resistivities $\\rho$ of the half space and the cylinder can be varied.\n",
+    "\n",
+    "2. Potential differences and Apparent Resistivities: Using the widgets contained in this notebook you will develop a better understand of what values are actually measured in a DC resistivity survey and how these measurements can be processed, plotted, inverted, and interpreted. The principles of Apparent Resistivity are introduced an further depicted in the widget.\n",
+    "\n",
+    "3. Building Pseudosections: 2D profiles are often plotted as pseudo-sections by extending $45^{\\circ}$ lines downwards from the A-B and M-N midpoints and plotting the corresponding $\\Delta V_{MN}$, $\\rho_a$, or misfit value at the intersection of these lines. Pseudosections build the foundtion of the following inversion. Pseudo-sections of the apparent resistivity can be generated using dipole-dipole, pole-dipole, or dipole-pole arrays to see how survey geometry can distort the size, shape, and location of conductive bodies in a pseudo-section.  \n",
+    "\n",
+    "4. Parametric Inversion: A pseudosection indicates how the parameter varies with location and depth, but it can only be converted into a 2D model by inversion. Inverting the data to find a model which fits the observed data and is geologically reasonable a standard practice. In this final widget you are able to forward model the apparent resistivity of a cylinder embedded in a two layered earth. \n",
+    "\n",
+    "\n",
+    "- [DC_LayeredEarth.ipynb](./Notebooks/dcip/DC_LayeredEarth.ipynb): \n",
+    "\n",
+    "Using the widgets contained in this notebook we will explore the physical principals governing DC resistivity including the behavior of currents, electric field, electric potentials in a two layer earth. The measured data in a DC experiment are potential differences, we will demonstrate how these provide information about subsurface physical properties. (ALREADY IN FIRST NOTEBOOK)\n",
+    "\n",
+    "- [DC_Cylinder_2D.ipynb](./Notebooks/dcip/DC_Cylinder_2D.ipynb): \n",
+    "\n",
+    "For a direct current resistivity (DCR) survey, currents are injected to the earth, and flow. Depending upon the conductivity contrast current flow in the earth will be distorted, and these changes can be measurable on the surface electrodes. Here, we focus on a cylinder target embedded in a halfspace, and investigate what is happening in the earth when static currents are injected. By investigating changes in currents, electric fields, potential, and charges upon different geometry of cylinder and survey, Tx and Rx location, we understand geometric effects of the target for DCR survey (ALREADY IN FIRST NOTEBOOK).\n",
+    "\n",
+    "- [DC_Layer_Cylinder_2D.ipynb](./Notebooks/dcip/DC_Layer_Cylinder_2D.ipynb): \n",
+    "\n",
+    "In some situations the presence of a near surface layer can have large implications for the detectability of targets beneath the layer. If the near surface layer is very conductive current channelling occrurs and when the layer is very resistive it has a shielding effect. In both cases the near surface layer dramatically reduces the strength of currents beneath the layer and therefore also reduces the strength of charge build up on the surface of the target. This notebook is simillary built up like the previous Notebook except for the addition of a near surface layer.\n",
+    "\n",
+    "- [PhyProp_ColeCole.ipynb](./Notebooks/dcip/PhyProp_ColeCole.ipynb): \n",
+    "\n",
+    "Using a simple Cole-Cole model, we parameterize complex resistivity with four parameters: resistivity at zero frequency ($\\rho_0$), chargeability($\\eta$), time constant ($\\tau$), and frequency dependence ($c$). Based upon those parameters, we understand how resistivity and conductivity changes when medium is chargeable both in frequency domain and time domain.\n",
+    "\n",
+    "### Electromagnetics\n",
+    "\n",
+    "#### Frequency domain (FDEM)\n",
+    "- [EM31.ipynb](./Notebooks/em/FEM/EM_EM31.ipynb): \n",
+    "\n",
+    "In this app, we compute apparent resistivity using the response curves for a two-loop Frequency domain system for a two-layer earth. Below figure shows horizontal coplanar (HCP) configuration. \n",
+    "\n",
+    "- [FDEM EM_Pipeline.ipynb](./Notebooks/em/FEM/EM_Pipeline.ipynb): \n",
+    "\n",
+    "In the following app, we consider a loop-loop system with a pipe taget. Here, we simulate two surveys, one where the boom is oriented East-West (EW) and one where the boom is oriented North-South (NS). \n",
+    "\n",
+    "- [FDEM_Planewave_Wholespace.ipynb](./Notebooks/em/FEM/FDEM_Planewave_Wholespace.ipynb): \n",
+    "\n",
+    "We visualizae downward propagating planewave in the homogeneous earth medium. With the three apps: a) Plane wave app, b) Profile app, and c) Polarization ellipse app, we understand fundamental concepts of planewave propagation. \n",
+    "\n",
+    "#### Time Domain (TDEM)\n",
+    "- [TDEM_Groundedsource.ipynb](./Notebooks/em/TEM/TDEM_Groundedsource.ipynb):\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from a grounded source. Both electric currents and magnetic flux will be visualized to undertand physics of grounded source EM. Both charge buildup (galvanic) and EM induction (inductive) will occur at different times. \n",
+    "\n",
+    "- [TDEM_HorizontalLoop_LayeredEarth.ipynb](./Notebooks/em/TEM/TDEM_HorizontalLoop_LayeredEarth.ipynb):\n",
+    "\n",
+    "Here, we show the transient fields and fluxes that result from placing a vertical magnetic dipole (VMD) source over a layered Earth. The transient response in this case refers to the fields and fluxes that are produced once a long-standing primary magnetic field is removed. There are [two commonly used models](https://em.geosci.xyz/content/maxwell1_fundamentals/dipole_sources_in_homogeneous_media/magnetic_dipole_time/index.html) for describing the VMD source that produces a transient response (both models are used in the Notebook): \n",
+    "\n",
+    "1) as an infinitessimally small bar magnet that experiences a long-standing vertical magnetization which is then instantaneously removed at $t=0$\n",
+    "\n",
+    "2) as an infinitessimally small horizontal loop of wire carrying a constant current which is then instantaneously shut off at $t=0$ (step-off current waveform).\n",
+    "\n",
+    "- [TDEM_InductiveSource.ipynb](./Notebooks/em/TEM/TDEM_InductiveSource.ipynb):\n",
+    "\n",
+    "We explore time-domain electromagnetic (EM) simulation results from inductive sources. Both electric currents and magnetic flux will be visualized to understand physics of inductive source EM. \n",
+    "\n",
+    "\n",
+    "\n",
+    "### Ground Penetrating Radar (GPR)\n",
+    "\n",
+    "- [GPR_Attenuation.ipynb](./Notebooks/gpr/GPR_Attenuation.ipynb):\n",
+    "\n",
+    "This Notebook focuses on the principles of EM wave attenuation. To simplify the GPR problems, we often assume that we do not have conductivity effect. However, in practice, this is not true. For instance, the earth medium can have considerably high conductivity values. In this case, EM wave attenuates as a function of conductivity ($\\sigma$), permittivity ($\\epsilon$), and frequency ($f$). How these factors influence attenuation is investigated in this Notebook.\n",
+    "\n",
+    "- [GPR_Lab6_FitData.ipynb](./Notebooks/gpr/GPR_Lab6_FitData.ipynb):\n",
+    "\n",
+    "This notebook contains two apps:\n",
+    "\n",
+    "+ **Pipe Fitting App**: This app simulates the radargram signature from a cylindrical pipe and lays it over a set of field collected data.\n",
+    "+ **Slab Fitting App**: This app simulates the radargram signature from a rectangular slab and lays it over a set of field collected data.\n",
+    "\n",
+    "By using the models provided (pipe/slab) to fit data signatures within field collected radargram data, we can determine the existence, location and dimensions of pipes and slabs. You may also use this app to learn how radargram signatures from pipes and rectangular slabs change as the parameters provided are altered.\n",
+    "\n",
+    "- [GPR_TBL4_DOI_Resolution.ipynb](./Notebooks/gpr/GPR_TBL4_DOI_Resolution.ipynb):\n",
+    "\n",
+    "This notebook contains two apps:\n",
+    "+ **GPR Zero Offset App**: This app simulates radargram data from two reflectors buried in a homogeneous Earth. The range of parameter values for this app are set such that we may assume we are operating in the wave regime.\n",
+    "+ **Attenuation App**: This app computes the propagation velocity and skin depth for GPR signals as a function of operating frequency.\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Magnetics\n",
+    "\n",
+    "- [Mag_Dipole.ipynb](./Notebooks/mag/Mag_Dipole.ipynb): \n",
+    "\n",
+    "Define a magnetic dipole, the Earth's magneitc field and the observation point to compute 3D plots of field lines and data. This notebook aims to provide the basic principles of magnetic methods in geophysics.\n",
+    "\n",
+    "- [MagneticDipoleApplet.ipynb](./Notebooks/mag/MagneticDipoleApplet.ipynb): \n",
+    "\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a small buried dipolar magnet. In geophysics, this simulates the observed anomaly over a buried susceptible sphere that is magnetized by the Earth's magnetic field.\n",
+    "\n",
+    "- [MagneticPrismApplet.ipynb](./Notebooks/mag/MagneticPrismApplet.ipynb): \n",
+    "\n",
+    "From the Magnetic Dipole applet, we have learned how anomalous magnetic field observed at ground's surface look\n",
+    "The objective is to learn about the magnetic field observed at the ground's surface, caused by a retangular susceptible prism. \n",
+    "\n",
+    "- [Mag_Induced2D.ipynb](./Notebooks/mag/Mag_Induced2D.ipynb): \n",
+    "\n",
+    "An induced magnetic anomaly can be modelled in this Notebook. The model of the is a rectangular prism. Its geometry and the height of the survey grid above the ground can be adjusted. Moreover, you can change the Earth's field characteristics as well. Based on the prism that you made as well as the defined Earth's magnetic field, the total magnetic field at the receiver locations is computed. In the end, a 2D map and a profile line is produced. \n",
+    "\n",
+    "- [Mag_FitProfile.ipynb](./Notebooks/mag/Mag_FitProfile.ipynb): \n",
+    "\n",
+    "In this Notebook, the fit of one magnetic profile from field observation can be performed.\n",
+    "\n",
+    "### Seismic\n",
+    "- [SeismicApplet.ipynb](./Notebooks/seismic/SeismicApplet.ipynb): \n",
+    "\n",
+    "This Notebooks allows you to model a simple subsurface model to interactively explore seismic raypaths dpending on the applied parameters.\n",
+    "\n",
+    "- [Seis_Refraction.ipynb](./Notebooks/seismic/Seis_Refraction.ipynb): \n",
+    "\n",
+    "A Seismic refraction survey is demonstrated. In this notebook, we will use synthetic seismic data to examine the impact of survey parameters on the expected seismic data.\n",
+    "\n",
+    "- [Seis_Reflection.ipynb](./Notebooks/seismic/Seis_Reflection.ipynb): \n",
+    "\n",
+    "A synthetic reflection seismogram is produced. This Notebook aims to introduce you to the basic principles of reflection seismics. This Notebook also includes vertical resolution and NMO correction widgets. \n",
+    "\n",
+    "- [Seis_NMO.ipynb](./Notebooks/seismic/Seis_NMO.ipynb): \n",
+    "\n",
+    "Consider a reflection event on a CMP gather. The difference between the two-way time at a given offset and the two-way zero-offset time is called normal moveout (NMO). Reflection traveltimes must be corrected for NMO prior to summing the traces in the CMP gather along the offset axis. \n",
+    "We have two CMP gathers generated from different geologic models. One data set is clean and the other is contaminated with noise. In this notebook, we will walk through how to construct a normal incidence seismogram from these data sets. The processing steps include plotting the data, fitting a hyperbola to the reflection event in the data, performin the NMO correction and stacking.\n",
+    "\n",
+    "- [Seis_VerticalResolution.ipynb](./Notebooks/seismic/Seis_VerticalResolution.ipynb): \n",
+    "\n",
+    "When referring to vertical resolution, the question whether two arrivals (one from the top, and one from the bottom of the layer) can be distinguished. In this Notebook, adjust the layer thickness for the middle layer and the frequency of the input pulse to investigate vertical resolution. You can also add noise to the trace. \n",
+    "\n",
+    "- [fourier_transform.ipynb](./Notebooks/seismic/fourier_transform.ipynb): \n",
+    "\n",
+    "In the world of seismology, we use the *Fourier transformation* to transform a signal from the time domain into the frequency domain. That means, we split up the signal and separate the content of each frequency from each other. Doing so, we can analyse our signal according to energy content per frequency. We can extract information on how much amplitude each frequency contributes to the final signal. \n",
+    "\n",
+    "- [2D-LinearInversion-Crosswell-Tomorgraphy.ipynb](./Notebooks/seismic/2D-LinearInversion-Crosswell-Tomorgraphy.ipynb):\n",
+    "\n",
+    "Real world geophysical inverse problems are multidimensional (2D or 3D). This extension of dimension allows us to put more apriori (or geologic) information through the regularization term.  In this notebook, we explore these multidimensional aspects of the linear inversion by using 2D traveltime croswell tomography example. \n",
+    "\n",
+    "\n",
+    "### Gravity\n",
+    "- [gravitySphere.ipynb](./Notebooks/gravity/gravitySphere.ipynb): \n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a sphere buried in the subsurface.\n",
+    "\n",
+    "- [gravityDike.ipynb](./Notebooks/gravity/gravityDike.ipynb): \n",
+    "\n",
+    "This notebook demonstrates the gravity anomaly generated by a 2D dipping dike that is infinite along one horizontal direction and buried in the subsurface. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### <center>We love open source!</center>\n",
+    "\n",
+    "<center><a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" width=60 src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a> \n",
+    "\n",
+    "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/requirements.txt b/requirements.txt
index 764e08f618d19c42391d8fb3cd049428d1705668..20307cb97f5cf326b6edd6eb90d542aefea158de 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,6 +6,7 @@ ipywidgets
 SimPEG>=0.14.1
 discretize>=0.4.14
 empymod>=2.0.0
+pandas==0.24
 jupyter
 ipywidgets
 deepdish
diff --git a/seismic/Seis_Refraction.ipynb b/seismic/Seis_Refraction.ipynb
deleted file mode 100644
index ae2b2af7538ee8b2ac88fdbbb649125de92d8383..0000000000000000000000000000000000000000
--- a/seismic/Seis_Refraction.ipynb
+++ /dev/null
@@ -1,297 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from geoscilabs.seismic.SeismicRefraction import (\n",
-    "    plotWavelet, viewTXdiagram, plotWiggleTX, makeinteractSeisRefracSurvey,\n",
-    "    makeinteractTXwigglediagram\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Interpretation and data acquisition strategies of seismic refraction data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the <a href=\"https://www.3ptscience.com/app/SeismicRefraction\">3pt Science app</a>, you explored the expected arrival times for refractions and reflections from a two-layer over a half-space model. \n",
-    "\n",
-    "In this notebook, we will use synthetic seismic data to examine the impact of survey parameters on the expected seismic data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Source "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In an ideal case, the source wavelet would be an impulse (ie. an instantaneous spike). However, in reality, the source energy is spread in space and in time (see the <a href=\"http://gpg.geosci.xyz/content/seismic/wave_basics.html#waves-and-rays\">GPG: Waves and Rays</a>). The source wavelet used for these examples is shown below. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 216x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'Source Wavelet'}, xlabel='Signal Amplitude', ylabel='time (s)'>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plotWavelet()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data\n",
-    "\n",
-    "Below, we show 3 plots:\n",
-    "- **left**: expected arrival times for the direct, refracted waves and reflection from the first layer\n",
-    "- **center**: clean data - the wavelet arriving at the expected arrival time. Each line represents what would be recorded by an ideal geophone.\n",
-    "- **right**: noisy data - clean data + random noise. \n",
-    "\n",
-    "The model used is the same as is in the lab write-up: \n",
-    "- v1 = 400 m/s\n",
-    "- v2 = 1000 m/s\n",
-    "- v3 = 1500 m/s\n",
-    "- z1 = 5m (depth to layer 1)\n",
-    "- z2 = 15m (depth to layer 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1, 3, figsize=(15,6))\n",
-    "ax[0].set_title('Expected Arrival Times')\n",
-    "ax[1].set_title('Clean Data')\n",
-    "ax[2].set_title('Noisy Data')\n",
-    "ax[0]=viewTXdiagram(x0=1., dx=8, v1=400., v2=1000., v3=1500., z1=5., z2=15., ax=ax[0])\n",
-    "ax[1]=plotWiggleTX(x0=1., dx=8, v1=400., v2=1000., v3=1500., z1=5., z2=15., ax=ax[1])\n",
-    "ax[2]=plotWiggleTX(x0=1., dx=8, v1=400., v2=1000., v3=1500., z1=5., z2=15., ax=ax[2], noise=True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup for the seismic refraction survey"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Consider a shot gather for seismic refraction survey, which means we have one shot (source), and multiple receivers (12). Shot location is fixed at x=0. There are two survey parameters: \n",
-    "\n",
-    "- x0: offset between shot and the first geophone\n",
-    "- dx: spacing between two consecutive geophones\n",
-    "\n",
-    "In the widget below you can alter x0 or dx to change your survey setup. Run the next cell then try to change x0 and dx in the cell below that. Note that the next two cells are designed to help you visualize the survey layout. The x0 and dx parameter adjustment sliders here are not linked to the widget at the end of this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2527ba625e124e389cae2fa4756b7a25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=0, description='x0', max=10), IntSlider(value=8, description='dx', max=1…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "makeinteractSeisRefracSurvey()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interpretation of seismic refraction data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assume that you have seismic refraction data. The structure of the earth is unknown and you may want to obtain useful information about the subsurface. We will assume that the subsurface in the survey area has a three-layer structure and that the velocities increase with depth. \n",
-    "Thus, there can be four unknowns:\n",
-    "\n",
-    "- v1: velocity of the first layer (m/s)\n",
-    "- v2: velocity of the second layer (m/s)\n",
-    "- v3: velocity of the third layer (m/s)\n",
-    "- z1: depth of the first layer (m)\n",
-    "- z2: depth of the second layer (m)\n",
-    "\n",
-    "Based on the above information, we may expect to have up to four arrivals at a geophone, related to \n",
-    "\n",
-    "- Direct\n",
-    "- Reflected: interface 1\n",
-    "- Refraction: interface 1\n",
-    "- Refraction: interface 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The widget below will allow you to estimate the layer depths and velocities. The parameters for the widget are:\n",
-    "\n",
-    "- x0: offset between shot and the first geophone\n",
-    "- dx: spacing between two consecutive geophones\n",
-    "- Fit: checking this activates fittting function (if you click this red line will show up)\n",
-    "- tI: intercept time for a line function (s)\n",
-    "- v: inverse slope of the line function (m/s; which can be velocity of either direct and critically refracted wave)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run below widget and find useful subsurface information!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8f3a610738544e9aeb0814fcf7b4bae",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=4, description='x0', max=10, min=1), IntSlider(value=4, description='dx'…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "makeinteractTXwigglediagram()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  },
-  "latex_envs": {
-   "bibliofile": "biblio.bib",
-   "cite_by": "apalike",
-   "current_citInitial": 1,
-   "eqLabelWithNumbers": true,
-   "eqNumInitial": 0
-  },
-  "widgets": {
-   "state": {
-    "58141af61d2a4d6393c0f5e35a09cccf": {
-     "views": [
-      {
-       "cell_index": 10
-      }
-     ]
-    },
-    "75727a01f50445469ade2c7092094a5b": {
-     "views": [
-      {
-       "cell_index": 15
-      }
-     ]
-    }
-   },
-   "version": "1.2.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}